Rehydrate: Restore Real Data into AI Analysis Locally

Where this fits

You've just run a cloud-based LLM analysis on a redacted copy of your data—every person's name replaced with [CONFIDE_PERSON_0001], every date with [CONFIDE_DATE_0002]. The output is useful but full of placeholders. Now you need the real values back, without sending the sensitive map to the cloud. The rehydrate skill does exactly that: it takes the analysis and your local *.map.json, and replaces placeholders with the original values—all on your machine.

Why agents benefit

• Completes the CONFIDE round-trip locally: redact → cloud-analyze → rehydrate, without ever exposing the map.
• No data exfiltration: the map never leaves the machine; nothing is fetched or transmitted, so you can use any LLM safely.
• Hallucination detection: warns about placeholders not present in the map, flagging potential LLM fabrication.
• Privacy-safe reporting: prints only counts of restored entries—never echoes the actual PII—keeping logs clean.
• Deterministic and reversible: works only with the user's own map, ensuring no guesswork or partial recovery.

Practical scenarios

Post‑analysis report cleanup

A support team sends a transcript to a cloud summarizer after redacting customer names. The summary references [CONFIDE_PERSON_0003] multiple times. The agent rehydrates the summary locally, restoring the actual names for internal use, while the cloud never sees them.

Compliance audit trail

An analyst produces a risk assessment from green text. The auditor needs to verify which real account was flagged. Using rehydrate, the agent swaps placeholders back into the assessment, producing a human-readable report while keeping the map on‑premises.

Multi‑step agent workflows

A developer builds an agent that first anonymizes a dataset, then runs a third‑party code review on the green version, and finally rehydrates the suggestions to reference real variable names. The skill slots in as the final step, ensuring the output is actionable without leaking sensitive identifiers.

Add it to your agent workflow

Install the skill via npx:

npx skills add glebis/claude-skills --skill rehydrate

Then, in your agent prompt, simply say: "Rehydrate the analysis using the map file data.map.json." The agent will read the map, replace all [CONFIDE_*] tokens, print a summary of replacements, and warn about any unmatched placeholders.

Compare before adopting

Before committing, evaluate the skill against your needs:

• Quality signals: Does it handle all placeholder types (person, date, email, etc.)? Does it verify SHA‑256 integrity of the green text?
• Maintenance freshness: Check the GitHub repo for recent commits and issue responses. A stale skill may break with new LLM output formats.
• Alternatives: Could you write a simple script? If you need just a one‑off replacement, a regex might suffice, but for robust round‑trips, this skill is purpose‑built.
• Workflow fit: Does it integrate with your existing agent's file‑handling and permission model? Since it runs locally, it must have access to the map file.

Why it is worth tracking

With 370 stars and an active author, rehydrate is part of a growing ecosystem of privacy‑preserving agent skills. Its focus on local execution and hallucination warnings addresses a real pain point for developers using cloud LLMs on sensitive data. Track it if you regularly handle PII and want a safe, reversible analysis pipeline. For a curated list of similar skills, see the OpenAgentSkill listing. Evaluate it when your workflows start involving redaction and you need a reliable way to restore context without compromising security.