Creator · glebis
Last updated · Sep 5, 2026
Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real n
Creator · glebis
Last updated · Sep 5, 2026
Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real n
Creator · glebis
Last updated · Sep 5, 2026
Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real n
Creator · glebis
Last updated · Sep 5, 2026
Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real n
Sandbox only
Install targets
Codex install prompt
Install the "rehydrate" agent skill from https://github.com/glebis/claude-skills/tree/main/confide/skills/rehydrate. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"glebis-rehydrate","task":"Install rehydrate","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add glebis/claude-skills --skill rehydrate
Maintenance
fresh
4d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
370
73/100 Quality · 69/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
370 GitHub stars
Repo activity
370 stars, 55 forks
Maintenance
4d since push
License
MIT
Install
npx skills add glebis/claude-skills --skill rehydrate
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add glebis/claude-skills --skill rehydrateDo not use when
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/glebis-rehydrate/install
Agent should check
Copy prompt
Task: Use rehydrate in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/glebis-rehydrate/install
Install command: npx skills add glebis/claude-skills --skill rehydrate
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/glebis-rehydrate/install
LLM text format
/api/skills/glebis-rehydrate/install?format=text
Find alternatives
/api/skills/search?q=rehydrate&limit=3
Agent prompt
Use rehydrate for this task. Review https://www.openagentskill.com/api/skills/glebis-rehydrate/install, then install with: npx skills add glebis/claude-skills --skill rehydrateRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/glebis-rehydrate
LLM text
/api/registry/manifest/glebis-rehydrate?format=text
Install alias
/api/registry/install/glebis-rehydrate
Recommend
/api/registry/recommend?task=Use%20rehydrate%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO370 GitHub stars
Stars/forks activity
INFO370 stars, 55 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
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Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: rehydrate description: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). ---
# confide:rehydrate — restore real values into a placeholder analysis
This is the last step of the CONFIDE round-trip:
1. **Redact** locally with `confide:anon` (default reversible mode) → a GREEN copy with unique reserved-sentinel placeholders (`[CONFIDE_PERSON_0001]`, `[CONFIDE_EMAIL_0001]`, `[CONFIDE_DATE_0002]`…) plus a secret `<name>.map.json` (structured: `green_sha256`, `entries[]`…) that stays on the machine. 2. **Analyze the GREEN text** anywhere — including a cloud LLM. The analysis comes back full of those same placeholders (sometimes mangled to `CONFIDE_PERSON_0001`, `[CONFIDE PERSON 0001]`… — but always keeping the full `CONFIDE_TYPE_NNNN` core). 3. **Rehydrate** here: replace every placeholder with its original value from your own map, producing the real analysis — without the cloud ever seeing the originals.
## Privacy invariants (do not violate) - **Local-only, on the user's OWN map.** The map never leaves the machine. This skill never fetches or transmits anything. - **Counts only on stdout.** The summary reports `restored N, unmatched M`. The restored text (which contains originals) is written to a local file; it is NEVER echoed to stdout beyond the counts. - **Unmatched = possible hallucination.** A placeholder that is not in the map is reported as `unmatched` and **left in place** — we never invent a value for it. Warn the user that these may be LLM hallucinations. - The `<name>.restored.md` output contains originals: it is local-only, do not share or commit it.
## Run it ```bash python3 skills/rehydrate/scripts/rehydrate.py ANALYSIS_FILE [--map <name>.map.json] ``` - `ANALYSIS_FILE` — the text/analysis containing placeholders. - `--map <name>.map.json` — the secret map from `confide:anon`. If omitted, the sibling `*.map.json` is auto-found (e.g. an analysis next to `session.map.json`, or one derived from `session.green.md`). - `--out PATH` — output path (default: `<name>.restored.md` next to the input). - `--verify-green <green_file>` — check the map's `green_sha256` against that GREEN file; warns loudly if they don't match (wrong map for this document).
Robust to LLM mangling that still keeps the full `CONFIDE_TYPE_NNNN` core: `[CONFIDE_PERSON_0001]`, `[CONFIDE PERSON 0001]`, `CONFIDE_PERSON_0001`, and `confide_person_0001` all restore to the same map entry. Ordinary prose lacking the `CONFIDE` prefix (e.g. "Person 1", "patient 1", "section 2") is **never** touched, and `0001` never eats `0010`. Rehydration is idempotent (re-running on restored text is a no-op). Accepts both the structured map schema and a legacy flat map.
## After running 1. Report the restore summary (`restored N, unmatched M`) — never paste PII. 2. If `unmatched > 0`, flag the placeholders as possible hallucinations not in the map. 3. Remind the user the `*.restored.md` is local-only (it now contains originals).
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for rehydrate, ready for a manual X post.
A practical pick for a repeatable workflow: rehydrate: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own r... 370 stars https://www.openagentskill.com/skills/glebis-rehydrate?ref=x
Listing + install path for rehydrate: https://www.openagentskill.com/skills/glebis-rehydrate?ref=x Install: npx skills add glebis/claude-skills --skill rehydrate
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to glebis but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate/audit)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)glebis
@glebis
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
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Install targets
Codex install prompt
Install the "rehydrate" agent skill from https://github.com/glebis/claude-skills/tree/main/confide/skills/rehydrate. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"glebis-rehydrate","task":"Install rehydrate","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add glebis/claude-skills --skill rehydrate
Maintenance
fresh
4d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
370
73/100 Quality · 69/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
370 GitHub stars
Repo activity
370 stars, 55 forks
Maintenance
4d since push
License
MIT
Install
npx skills add glebis/claude-skills --skill rehydrate
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add glebis/claude-skills --skill rehydrateDo not use when
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/glebis-rehydrate/install
Agent should check
Copy prompt
Task: Use rehydrate in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/glebis-rehydrate/install
Install command: npx skills add glebis/claude-skills --skill rehydrate
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/glebis-rehydrate/install
LLM text format
/api/skills/glebis-rehydrate/install?format=text
Find alternatives
/api/skills/search?q=rehydrate&limit=3
Agent prompt
Use rehydrate for this task. Review https://www.openagentskill.com/api/skills/glebis-rehydrate/install, then install with: npx skills add glebis/claude-skills --skill rehydrateRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/glebis-rehydrate
LLM text
/api/registry/manifest/glebis-rehydrate?format=text
Install alias
/api/registry/install/glebis-rehydrate
Recommend
/api/registry/recommend?task=Use%20rehydrate%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO370 GitHub stars
Stars/forks activity
INFO370 stars, 55 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: rehydrate description: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). ---
# confide:rehydrate — restore real values into a placeholder analysis
This is the last step of the CONFIDE round-trip:
1. **Redact** locally with `confide:anon` (default reversible mode) → a GREEN copy with unique reserved-sentinel placeholders (`[CONFIDE_PERSON_0001]`, `[CONFIDE_EMAIL_0001]`, `[CONFIDE_DATE_0002]`…) plus a secret `<name>.map.json` (structured: `green_sha256`, `entries[]`…) that stays on the machine. 2. **Analyze the GREEN text** anywhere — including a cloud LLM. The analysis comes back full of those same placeholders (sometimes mangled to `CONFIDE_PERSON_0001`, `[CONFIDE PERSON 0001]`… — but always keeping the full `CONFIDE_TYPE_NNNN` core). 3. **Rehydrate** here: replace every placeholder with its original value from your own map, producing the real analysis — without the cloud ever seeing the originals.
## Privacy invariants (do not violate) - **Local-only, on the user's OWN map.** The map never leaves the machine. This skill never fetches or transmits anything. - **Counts only on stdout.** The summary reports `restored N, unmatched M`. The restored text (which contains originals) is written to a local file; it is NEVER echoed to stdout beyond the counts. - **Unmatched = possible hallucination.** A placeholder that is not in the map is reported as `unmatched` and **left in place** — we never invent a value for it. Warn the user that these may be LLM hallucinations. - The `<name>.restored.md` output contains originals: it is local-only, do not share or commit it.
## Run it ```bash python3 skills/rehydrate/scripts/rehydrate.py ANALYSIS_FILE [--map <name>.map.json] ``` - `ANALYSIS_FILE` — the text/analysis containing placeholders. - `--map <name>.map.json` — the secret map from `confide:anon`. If omitted, the sibling `*.map.json` is auto-found (e.g. an analysis next to `session.map.json`, or one derived from `session.green.md`). - `--out PATH` — output path (default: `<name>.restored.md` next to the input). - `--verify-green <green_file>` — check the map's `green_sha256` against that GREEN file; warns loudly if they don't match (wrong map for this document).
Robust to LLM mangling that still keeps the full `CONFIDE_TYPE_NNNN` core: `[CONFIDE_PERSON_0001]`, `[CONFIDE PERSON 0001]`, `CONFIDE_PERSON_0001`, and `confide_person_0001` all restore to the same map entry. Ordinary prose lacking the `CONFIDE` prefix (e.g. "Person 1", "patient 1", "section 2") is **never** touched, and `0001` never eats `0010`. Rehydration is idempotent (re-running on restored text is a no-op). Accepts both the structured map schema and a legacy flat map.
## After running 1. Report the restore summary (`restored N, unmatched M`) — never paste PII. 2. If `unmatched > 0`, flag the placeholders as possible hallucinations not in the map. 3. Remind the user the `*.restored.md` is local-only (it now contains originals).
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for rehydrate, ready for a manual X post.
A practical pick for a repeatable workflow: rehydrate: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own r... 370 stars https://www.openagentskill.com/skills/glebis-rehydrate?ref=x
Listing + install path for rehydrate: https://www.openagentskill.com/skills/glebis-rehydrate?ref=x Install: npx skills add glebis/claude-skills --skill rehydrate
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to glebis but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate/audit)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)glebis
@glebis
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K Starsn8n
Connect agents to hundreds of workflow automations
194.1K StarsMoneyPrinterTurbo
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarsTasmota
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
24.7K StarsSandbox only
Install targets
Codex install prompt
Install the "rehydrate" agent skill from https://github.com/glebis/claude-skills/tree/main/confide/skills/rehydrate. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"glebis-rehydrate","task":"Install rehydrate","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add glebis/claude-skills --skill rehydrate
Maintenance
fresh
4d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
370
73/100 Quality · 69/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
370 GitHub stars
Repo activity
370 stars, 55 forks
Maintenance
4d since push
License
MIT
Install
npx skills add glebis/claude-skills --skill rehydrate
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add glebis/claude-skills --skill rehydrateDo not use when
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/glebis-rehydrate/install
Agent should check
Copy prompt
Task: Use rehydrate in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/glebis-rehydrate/install
Install command: npx skills add glebis/claude-skills --skill rehydrate
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/glebis-rehydrate/install
LLM text format
/api/skills/glebis-rehydrate/install?format=text
Find alternatives
/api/skills/search?q=rehydrate&limit=3
Agent prompt
Use rehydrate for this task. Review https://www.openagentskill.com/api/skills/glebis-rehydrate/install, then install with: npx skills add glebis/claude-skills --skill rehydrateRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/glebis-rehydrate
LLM text
/api/registry/manifest/glebis-rehydrate?format=text
Install alias
/api/registry/install/glebis-rehydrate
Recommend
/api/registry/recommend?task=Use%20rehydrate%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO370 GitHub stars
Stars/forks activity
INFO370 stars, 55 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: rehydrate description: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). ---
# confide:rehydrate — restore real values into a placeholder analysis
This is the last step of the CONFIDE round-trip:
1. **Redact** locally with `confide:anon` (default reversible mode) → a GREEN copy with unique reserved-sentinel placeholders (`[CONFIDE_PERSON_0001]`, `[CONFIDE_EMAIL_0001]`, `[CONFIDE_DATE_0002]`…) plus a secret `<name>.map.json` (structured: `green_sha256`, `entries[]`…) that stays on the machine. 2. **Analyze the GREEN text** anywhere — including a cloud LLM. The analysis comes back full of those same placeholders (sometimes mangled to `CONFIDE_PERSON_0001`, `[CONFIDE PERSON 0001]`… — but always keeping the full `CONFIDE_TYPE_NNNN` core). 3. **Rehydrate** here: replace every placeholder with its original value from your own map, producing the real analysis — without the cloud ever seeing the originals.
## Privacy invariants (do not violate) - **Local-only, on the user's OWN map.** The map never leaves the machine. This skill never fetches or transmits anything. - **Counts only on stdout.** The summary reports `restored N, unmatched M`. The restored text (which contains originals) is written to a local file; it is NEVER echoed to stdout beyond the counts. - **Unmatched = possible hallucination.** A placeholder that is not in the map is reported as `unmatched` and **left in place** — we never invent a value for it. Warn the user that these may be LLM hallucinations. - The `<name>.restored.md` output contains originals: it is local-only, do not share or commit it.
## Run it ```bash python3 skills/rehydrate/scripts/rehydrate.py ANALYSIS_FILE [--map <name>.map.json] ``` - `ANALYSIS_FILE` — the text/analysis containing placeholders. - `--map <name>.map.json` — the secret map from `confide:anon`. If omitted, the sibling `*.map.json` is auto-found (e.g. an analysis next to `session.map.json`, or one derived from `session.green.md`). - `--out PATH` — output path (default: `<name>.restored.md` next to the input). - `--verify-green <green_file>` — check the map's `green_sha256` against that GREEN file; warns loudly if they don't match (wrong map for this document).
Robust to LLM mangling that still keeps the full `CONFIDE_TYPE_NNNN` core: `[CONFIDE_PERSON_0001]`, `[CONFIDE PERSON 0001]`, `CONFIDE_PERSON_0001`, and `confide_person_0001` all restore to the same map entry. Ordinary prose lacking the `CONFIDE` prefix (e.g. "Person 1", "patient 1", "section 2") is **never** touched, and `0001` never eats `0010`. Rehydration is idempotent (re-running on restored text is a no-op). Accepts both the structured map schema and a legacy flat map.
## After running 1. Report the restore summary (`restored N, unmatched M`) — never paste PII. 2. If `unmatched > 0`, flag the placeholders as possible hallucinations not in the map. 3. Remind the user the `*.restored.md` is local-only (it now contains originals).
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for rehydrate, ready for a manual X post.
A practical pick for a repeatable workflow: rehydrate: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own r... 370 stars https://www.openagentskill.com/skills/glebis-rehydrate?ref=x
Listing + install path for rehydrate: https://www.openagentskill.com/skills/glebis-rehydrate?ref=x Install: npx skills add glebis/claude-skills --skill rehydrate
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to glebis but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate/audit)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)glebis
@glebis
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K Starsn8n
Connect agents to hundreds of workflow automations
194.1K StarsMoneyPrinterTurbo
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarsTasmota
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
24.7K StarsSandbox only
Install targets
Codex install prompt
Install the "rehydrate" agent skill from https://github.com/glebis/claude-skills/tree/main/confide/skills/rehydrate. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"glebis-rehydrate","task":"Install rehydrate","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add glebis/claude-skills --skill rehydrate
Maintenance
fresh
4d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
370
73/100 Quality · 69/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
370 GitHub stars
Repo activity
370 stars, 55 forks
Maintenance
4d since push
License
MIT
Install
npx skills add glebis/claude-skills --skill rehydrate
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add glebis/claude-skills --skill rehydrateDo not use when
Agent safety v2
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/glebis-rehydrate/install
Agent should check
Copy prompt
Task: Use rehydrate in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20rehydrate%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/glebis-rehydrate/install
Install command: npx skills add glebis/claude-skills --skill rehydrate
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/glebis-rehydrate/install
LLM text format
/api/skills/glebis-rehydrate/install?format=text
Find alternatives
/api/skills/search?q=rehydrate&limit=3
Agent prompt
Use rehydrate for this task. Review https://www.openagentskill.com/api/skills/glebis-rehydrate/install, then install with: npx skills add glebis/claude-skills --skill rehydrateRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/glebis-rehydrate
LLM text
/api/registry/manifest/glebis-rehydrate?format=text
Install alias
/api/registry/install/glebis-rehydrate
Recommend
/api/registry/recommend?task=Use%20rehydrate%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Research agents
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO370 GitHub stars
Stars/forks activity
INFO370 stars, 55 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: rehydrate description: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own reversible map. Completes the confide round-trip (redact -> cloud-analyze the green -> rehydrate locally). Use when the user says "rehydrate", "restore real names", "unmask the analysis", "put the names back", "de-redact this output", "reverse the placeholders", or hands you an analysis full of [CONFIDE_PERSON_0001]/[CONFIDE_DATE_0002] plus a *.map.json. Runs only on the user's own map; the map never leaves the machine; nothing fetched or transmitted. Prints counts only — never echoes restored PII. Warns on placeholders not in the map (possible LLM hallucination). ---
# confide:rehydrate — restore real values into a placeholder analysis
This is the last step of the CONFIDE round-trip:
1. **Redact** locally with `confide:anon` (default reversible mode) → a GREEN copy with unique reserved-sentinel placeholders (`[CONFIDE_PERSON_0001]`, `[CONFIDE_EMAIL_0001]`, `[CONFIDE_DATE_0002]`…) plus a secret `<name>.map.json` (structured: `green_sha256`, `entries[]`…) that stays on the machine. 2. **Analyze the GREEN text** anywhere — including a cloud LLM. The analysis comes back full of those same placeholders (sometimes mangled to `CONFIDE_PERSON_0001`, `[CONFIDE PERSON 0001]`… — but always keeping the full `CONFIDE_TYPE_NNNN` core). 3. **Rehydrate** here: replace every placeholder with its original value from your own map, producing the real analysis — without the cloud ever seeing the originals.
## Privacy invariants (do not violate) - **Local-only, on the user's OWN map.** The map never leaves the machine. This skill never fetches or transmits anything. - **Counts only on stdout.** The summary reports `restored N, unmatched M`. The restored text (which contains originals) is written to a local file; it is NEVER echoed to stdout beyond the counts. - **Unmatched = possible hallucination.** A placeholder that is not in the map is reported as `unmatched` and **left in place** — we never invent a value for it. Warn the user that these may be LLM hallucinations. - The `<name>.restored.md` output contains originals: it is local-only, do not share or commit it.
## Run it ```bash python3 skills/rehydrate/scripts/rehydrate.py ANALYSIS_FILE [--map <name>.map.json] ``` - `ANALYSIS_FILE` — the text/analysis containing placeholders. - `--map <name>.map.json` — the secret map from `confide:anon`. If omitted, the sibling `*.map.json` is auto-found (e.g. an analysis next to `session.map.json`, or one derived from `session.green.md`). - `--out PATH` — output path (default: `<name>.restored.md` next to the input). - `--verify-green <green_file>` — check the map's `green_sha256` against that GREEN file; warns loudly if they don't match (wrong map for this document).
Robust to LLM mangling that still keeps the full `CONFIDE_TYPE_NNNN` core: `[CONFIDE_PERSON_0001]`, `[CONFIDE PERSON 0001]`, `CONFIDE_PERSON_0001`, and `confide_person_0001` all restore to the same map entry. Ordinary prose lacking the `CONFIDE` prefix (e.g. "Person 1", "patient 1", "section 2") is **never** touched, and `0001` never eats `0010`. Rehydration is idempotent (re-running on restored text is a no-op). Accepts both the structured map schema and a legacy flat map.
## After running 1. Report the restore summary (`restored N, unmatched M`) — never paste PII. 2. If `unmatched > 0`, flag the placeholders as possible hallucinations not in the map. 3. Remind the user the `*.restored.md` is local-only (it now contains originals).
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for rehydrate, ready for a manual X post.
A practical pick for a repeatable workflow: rehydrate: Put the real values back into an analysis that was produced from GREEN (placeholder) text — LOCALLY, using the user's own r... 370 stars https://www.openagentskill.com/skills/glebis-rehydrate?ref=x
Listing + install path for rehydrate: https://www.openagentskill.com/skills/glebis-rehydrate?ref=x Install: npx skills add glebis/claude-skills --skill rehydrate
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to glebis but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/glebis-rehydrate/audit)
[](https://www.openagentskill.com/skills/glebis-rehydrate?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)glebis
@glebis
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K Starsn8n
Connect agents to hundreds of workflow automations
194.1K StarsMoneyPrinterTurbo
利用AI大模型,一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarsTasmota
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
24.7K StarsPermission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness