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shadow-frog-update
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ShadowFrog Update
Updates .shadow/ from two sources: code changes (git diff) and conversational
knowledge (what the user said during the session). Prerequisite: .shadow/ exists.
Triggers
- Hook reminder:
preToolUseinjects a staleness warning when.shadow/_meta/state.json#last_commitdiffers from HEAD. The hook only reminds — it does NOT auto-run update. - Manual: user invokes
/shadow-frog-update
Phase 1: Detect Changes
# Read last_commit defensively: it may be missing, or the literal "none"
# when init ran without git (e.g. inside a container). `git diff none HEAD`
# would abort with "fatal: bad revision 'none'", and a missing key would
# make $LAST_COMMIT empty so `git diff HEAD` silently reports the wrong set.
LAST_COMMIT=$(python3 -c "import json,sys; print(json.load(sys.stdin).get('last_commit','none'))" < .shadow/_meta/state.json 2>/dev/null || echo "none")
if git rev-parse --verify "$LAST_COMMIT" >/dev/null 2>&1; then
git diff --name-only "$LAST_COMMIT" HEAD # committed changes since last update
else
echo "WARNING: state.json has no usable last_commit — falling back to a full re-scan."
fi
git diff --name-only HEAD # uncommitted changes
git diff --name-only --cached # staged changes
Categorize: modified, added, deleted, renamed.
Phase 2: Update Per-File Shadows (Symbol-Level)
For each changed file, update its shadow at the symbol level:
- Added symbols → add new
##section - Removed symbols → mark section as
REMOVED, keep discoveries for history - Renamed symbols → update heading, preserve discoveries
- Modified symbols → check if discoveries still hold
Lightweight update (auto/hook): re-extract symbols, update headings, flag stale. Deep update (manual/dream): read diffs, generate new discoveries, verify existing ones.
Phase 3: Capture Conversational Knowledge
When the user shares knowledge during the session, write it immediately. Do not batch for later.
Signals to capture:
| Signal | Example | Category |
|---|---|---|
| Warning | "Don't change the retry logic, it's subtle" | warning |
| Design intent | "We use this pattern because the API is unreliable" | intent |
| History | "We tried caching here but it caused stale reads" | history |
| Gotcha | "This looks wrong but matches the tax authority spec" | warning |
| Deprecation | "This module is being replaced by v2/" | intent |
| Contract | "The 30s timeout matches our SLA" | contract |
| Convention | "Always use the helper in utils.py, not raw SQL" | convention |
Write as:
- <user's words, as close to verbatim as possible>
_(verified, source: user)_
For knowledge emerging from collaborative work (debugging, refactoring, test failures):
- <what was discovered and how>
_(verified, source: interaction)_
Anchor to the specific file::symbol. source: user and source: interaction
are always verified.
Auto-Placement
Users will not specify where to store their knowledge. You must find the correct location. Procedure:
- Parse the user's statement for code references — file names, function names, class names, module names, variable names, error messages, CLI flags.
- If the knowledge is a project-wide preference or convention with no code
references (e.g., "always use snake_case", "no backward compatibility",
"prefer small PRs") → write to
_prefs.md. - If explicit references found → look up those
file::symbolpaths in_index.mdand the corresponding shadow files. - If no explicit references → use context:
- What file is the user currently viewing or editing?
- What files were recently modified in this session?
- Search shadow files:
grep -rl "<keyword>" .shadow/ --include="*.md"
- If multiple candidate locations → pick the most specific symbol that the
knowledge applies to. Prefer a single
file::symbolover file-level. - If the knowledge spans 3+ files → create a
_cross/<slug>.mdentry and add back-pointers to each involved file's## Cross-References. For 2-file discoveries, use per-file entries withAlso involves:instead. - If no matching location exists (e.g., the user mentions a concept not yet in
the shadow) → place at the file-level
## File-Levelsection of the most relevant file, or create a new_cross/entry for repo-wide knowledge.
Never ask the user "where should I put this?" — always resolve placement yourself.
Phase 4: Extract Session Insights
At session end or manual trigger, review the session for:
- Files modified and why
- Patterns revealed by the changes
- Unrecorded conversational knowledge (user statements not yet shadowed)
- Cross-cutting discoveries (create in
_cross/<slug>.mdif 3+ files involved)
Phase 5: Handle Structural Changes
Added files:
- Check
.shadow/.shadowignore— skip if the file matches an ignore pattern - Create
.shadow/<path>/<file>.mdwith symbol-organized template - Add
## Cross-Referencessection - Add to
_index.md
Deleted files:
- Add
ORPHANEDmarker to shadow header - Keep shadow (discoveries explain history)
- Mark
[REMOVED]on any_cross/refs pointing to this file - Update
_index.md
Renamed files:
- Move
.shadow/<old>.mdto.shadow/<new>.md - Update all
_cross/**Refs**:entries (old path → new path) - Update all
Also involves:in other per-file shadows - Update
_index.md - Preserve all discoveries
Phase 6: Verify and Dedup
Follow the dedup and writing rules in /shadow-frog — read before
write, merge or update existing entries, fix bad format in place.
Verify exploration discoveries using the observe-based or do-based
methods in /shadow-frog § Verification. source: user and
source: interaction → always verified; only re-verify if the
underlying code changes.
Phase 7: Verify Reference Integrity
Check the five core invariants (full 7-invariant set in /shadow-frog):
- Every
_cross/<slug>.mdref has a back-pointer in per-file## Cross-References - Every
## Cross-Referencesentry has a corresponding_cross/<slug>.md - No duplicate cross-cutting filenames
- All
Also involves:usefile::symbolnotation - No duplicate discoveries (same behavioral claim at same symbol)
Repair any violations before proceeding.
Phase 8: Update Metadata
Preserve dream_cycles_completed from the existing state — only dream-reconcile.py increments it.
{
"version": 1,
"initialized_at": "<preserved>",
"last_update_at": "<now ISO>",
"last_commit": "<full 40-char HEAD SHA>",
"last_update_type": "init|auto|manual|dream|meditate",
"total_files": N,
"total_symbols": N,
"total_discoveries": N,
"dream_cycles_completed": <preserved>
}
Refresh _index.md with current counts.
Discovery Writing Rules
See /shadow-frog § Discovery Format for the verbatim per-file,
cross-cutting, and preference formats. Rules to keep in mind during
update sessions:
- Be behavioral: "silently returns None on expired tokens" not "handles token expiration"
source: userandsource: interaction→ alwaysverified, use user's own wordssource: exploration→ markuncertainunless verified by code reading or tests- If 3+ files involved → create in
_cross/<slug>.mdinstead, add back-pointers - If project-wide preference with no file reference → write to
_prefs.md - Slug naming: kebab-case derived from title (e.g., "Token expiry config split" →
token-expiry-config-split.md)
Staleness Rules
- Symbol modified → check if discovery still holds
- Symbol renamed → move discoveries to new heading
- Symbol removed → mark section
REMOVED, keep discoveries source: userdiscoveries → only mark stale if symbol completely removed
File metadata
name: shadow-frog-update description: >- Update the shadow knowledge base after code changes and from conversational insights. Detects what changed via git diff, refreshes per-file shadows, captures knowledge shared by the user during the session, and updates cross-cutting discoveries. Invoke manually with /shadow-frog-update; the preToolUse hook will remind the agent when the shadow is behind HEAD.
View original text
---
name: shadow-frog-update
description: >-
Update the shadow knowledge base after code changes and from conversational
insights. Detects what changed via git diff, refreshes per-file shadows,
captures knowledge shared by the user during the session, and updates
cross-cutting discoveries. Invoke manually with /shadow-frog-update; the
preToolUse hook will remind the agent when the shadow is behind HEAD.
---
# ShadowFrog Update
Updates `.shadow/` from two sources: code changes (git diff) and conversational
knowledge (what the user said during the session). Prerequisite: `.shadow/` exists.
## Triggers
1. Hook reminder: `preToolUse` injects a staleness warning when
`.shadow/_meta/state.json#last_commit` differs from HEAD. The hook
only reminds — it does NOT auto-run update.
2. Manual: user invokes `/shadow-frog-update`
## Phase 1: Detect Changes
```bash
# Read last_commit defensively: it may be missing, or the literal "none"
# when init ran without git (e.g. inside a container). `git diff none HEAD`
# would abort with "fatal: bad revision 'none'", and a missing key would
# make $LAST_COMMIT empty so `git diff HEAD` silently reports the wrong set.
LAST_COMMIT=$(python3 -c "import json,sys; print(json.load(sys.stdin).get('last_commit','none'))" < .shadow/_meta/state.json 2>/dev/null || echo "none")
if git rev-parse --verify "$LAST_COMMIT" >/dev/null 2>&1; then
git diff --name-only "$LAST_COMMIT" HEAD # committed changes since last update
else
echo "WARNING: state.json has no usable last_commit — falling back to a full re-scan."
fi
git diff --name-only HEAD # uncommitted changes
git diff --name-only --cached # staged changes
```
Categorize: modified, added, deleted, renamed.
## Phase 2: Update Per-File Shadows (Symbol-Level)
For each changed file, update its shadow at the symbol level:
- **Added symbols** → add new `##` section
- **Removed symbols** → mark section as `REMOVED`, keep discoveries for history
- **Renamed symbols** → update heading, preserve discoveries
- **Modified symbols** → check if discoveries still hold
Lightweight update (auto/hook): re-extract symbols, update headings, flag stale.
Deep update (manual/dream): read diffs, generate new discoveries, verify existing ones.
## Phase 3: Capture Conversational Knowledge
When the user shares knowledge during the session, write it immediately.
Do not batch for later.
Signals to capture:
| Signal | Example | Category |
|--------|---------|----------|
| Warning | "Don't change the retry logic, it's subtle" | warning |
| Design intent | "We use this pattern because the API is unreliable" | intent |
| History | "We tried caching here but it caused stale reads" | history |
| Gotcha | "This looks wrong but matches the tax authority spec" | warning |
| Deprecation | "This module is being replaced by v2/" | intent |
| Contract | "The 30s timeout matches our SLA" | contract |
| Convention | "Always use the helper in utils.py, not raw SQL" | convention |
Write as:
```markdown
- <user's words, as close to verbatim as possible>
_(verified, source: user)_
```
For knowledge emerging from collaborative work (debugging, refactoring, test failures):
```markdown
- <what was discovered and how>
_(verified, source: interaction)_
```
Anchor to the specific `file::symbol`. `source: user` and `source: interaction`
are always `verified`.
### Auto-Placement
Users will not specify where to store their knowledge. You must find the
correct location. Procedure:
1. Parse the user's statement for code references — file names, function names,
class names, module names, variable names, error messages, CLI flags.
2. If the knowledge is a **project-wide preference or convention** with no code
references (e.g., "always use snake_case", "no backward compatibility",
"prefer small PRs") → write to `_prefs.md`.
3. If explicit references found → look up those `file::symbol` paths in
`_index.md` and the corresponding shadow files.
4. If no explicit references → use context:
- What file is the user currently viewing or editing?
- What files were recently modified in this session?
- Search shadow files: `grep -rl "<keyword>" .shadow/ --include="*.md"`
5. If multiple candidate locations → pick the most specific symbol that the
knowledge applies to. Prefer a single `file::symbol` over file-level.
6. If the knowledge spans 3+ files → create a `_cross/<slug>.md`
entry and add back-pointers to each involved file's `## Cross-References`.
For 2-file discoveries, use per-file entries with `Also involves:` instead.
7. If no matching location exists (e.g., the user mentions a concept not yet in
the shadow) → place at the file-level `## File-Level` section of the most
relevant file, or create a new `_cross/` entry for repo-wide knowledge.
Never ask the user "where should I put this?" — always resolve placement yourself.
## Phase 4: Extract Session Insights
At session end or manual trigger, review the session for:
1. Files modified and why
2. Patterns revealed by the changes
3. Unrecorded conversational knowledge (user statements not yet shadowed)
4. Cross-cutting discoveries (create in `_cross/<slug>.md` if 3+ files involved)
## Phase 5: Handle Structural Changes
Added files:
1. Check `.shadow/.shadowignore` — skip if the file matches an ignore pattern
2. Create `.shadow/<path>/<file>.md` with symbol-organized template
3. Add `## Cross-References` section
4. Add to `_index.md`
Deleted files:
1. Add `ORPHANED` marker to shadow header
2. Keep shadow (discoveries explain history)
3. Mark `[REMOVED]` on any `_cross/` refs pointing to this file
4. Update `_index.md`
Renamed files:
1. Move `.shadow/<old>.md` to `.shadow/<new>.md`
2. Update all `_cross/` `**Refs**:` entries (old path → new path)
3. Update all `Also involves:` in other per-file shadows
4. Update `_index.md`
5. Preserve all discoveries
## Phase 6: Verify and Dedup
Follow the dedup and writing rules in `/shadow-frog` — read before
write, merge or update existing entries, fix bad format in place.
**Verify exploration discoveries** using the observe-based or do-based
methods in `/shadow-frog` § Verification. `source: user` and
`source: interaction` → always `verified`; only re-verify if the
underlying code changes.
## Phase 7: Verify Reference Integrity
Check the five core invariants (full 7-invariant set in `/shadow-frog`):
- Every `_cross/<slug>.md` ref has a back-pointer in per-file `## Cross-References`
- Every `## Cross-References` entry has a corresponding `_cross/<slug>.md`
- No duplicate cross-cutting filenames
- All `Also involves:` use `file::symbol` notation
- No duplicate discoveries (same behavioral claim at same symbol)
Repair any violations before proceeding.
## Phase 8: Update Metadata
Preserve `dream_cycles_completed` from the existing state — only `dream-reconcile.py` increments it.
```json
{
"version": 1,
"initialized_at": "<preserved>",
"last_update_at": "<now ISO>",
"last_commit": "<full 40-char HEAD SHA>",
"last_update_type": "init|auto|manual|dream|meditate",
"total_files": N,
"total_symbols": N,
"total_discoveries": N,
"dream_cycles_completed": <preserved>
}
```
Refresh `_index.md` with current counts.
## Discovery Writing Rules
See `/shadow-frog` § Discovery Format for the verbatim per-file,
cross-cutting, and preference formats. Rules to keep in mind during
update sessions:
- Be behavioral: "silently returns None on expired tokens" not "handles token expiration"
- `source: user` and `source: interaction` → always `verified`, use user's own words
- `source: exploration` → mark `uncertain` unless verified by code reading or tests
- If 3+ files involved → create in `_cross/<slug>.md` instead, add back-pointers
- If project-wide preference with no file reference → write to `_prefs.md`
- Slug naming: kebab-case derived from title (e.g., "Token expiry config split" → `token-expiry-config-split.md`)
## Staleness Rules
- Symbol modified → check if discovery still holds
- Symbol renamed → move discoveries to new heading
- Symbol removed → mark section `REMOVED`, keep discoveries
- `source: user` discoveries → only mark stale if symbol completely removed
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- MIT
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 23 GitHub stars
- Stars/forks activity: 23 stars, 7 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- microsoft/ShadowFrog
- License
- MIT
- Version
- Unknown
- Last GitHub push
- Sep 3, 2026
- Registry updated
- Sep 14, 2026
- Instruction path
- skills/shadow-frog-update/SKILL.md @ 6ae4fc8c6bdd
Version reported in registry metadata; check source releases before relying on it.
Quality
52/100
Needs review
Trust
53/100
Do not auto-install
Audit
66/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 23 GitHub stars
- Stars/forks activity: 23 stars, 7 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
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.
More details
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"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 66,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Low GitHub adoption signal",
"AI review approval is missing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"GitHub adoption: 23 GitHub stars",
"Stars/forks activity: 23 stars, 7 forks; issue activity unavailable in current metadata"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 52,
"label": "Needs review"
},
"supply": {
"track": "Data, BI, and analytics",
"scenario": "Browser automation",
"maintenance": "1mo since push",
"risk": "Needs review"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"Low GitHub adoption signal",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"AI review approval is missing",
"Quality score needs review"
],
"agent_contract": {
"task_input": "Use shadow-frog-update in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 61/100 Manual review",
"Audit: 66/100 Needs review",
"Safety: 22/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "microsoft-shadow-frog-update (shadow-frog-update)",
"install_command": "npx skills add microsoft/ShadowFrog --skill shadow-frog-update",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "microsoft-shadow-frog-update",
"task": "Use shadow-frog-update in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/microsoft-shadow-frog-update",
"api": "https://www.openagentskill.com/api/agent/skills/microsoft-shadow-frog-update",
"audit": "https://www.openagentskill.com/skills/microsoft-shadow-frog-update/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=microsoft-shadow-frog-update&task=Use%20shadow-frog-update%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20shadow-frog-update%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20shadow-frog-update%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/microsoft-shadow-frog-update/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/microsoft-shadow-frog-update"
}
}For the creator
Listing source
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This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- microsoft
- Source
- microsoft/ShadowFrog
- Indexed by
- OpenAgentSkill community index
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