Registry indexed

worklog-logging

Price unconfirmed★ 30 GitHub starsRegistry updated · Sep 11, 2026agent-skill

Overview

Lightweight work logger that captures what you accomplished in each Claude session. TRIGGER THIS SKILL when any of the following occur: (1) A session is ending or context is about to be compacted — capture what was done before it's lost. (2) At periodic context checkpoints (~25%, ~50%, ~75%) — routine logging, not a signal to stop. (3) The self-improve skill fires — piggyback on that trigger to also log work. (4) The user says "log this", "worklog", or wants to record what they've been doing. This skill ONLY handles logging — for standups, weekly summaries, monthly reviews, or any analysis of past work, use the worklog-analysis skill instead. Use this skill liberally. It's cheap to log and expensive to forget. IMPORTANT: After logging at a periodic checkpoint, resume the current task immediately. Never suggest ending the session, starting fresh, or doing a handoff.

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Worklog Logging

Capture what was accomplished in this session. This skill is intentionally lightweight — it logs and gets out of the way.

Storage

Files go to ~/Documents/AI/worklog/ for cross-device sync:

~/Documents/AI/worklog/
├── 2026-03-08-macbook-pro.md
├── 2026-03-08-mac-mini.md
├── 2026-03-07-macbook-pro.md
└── ...

Naming: YYYY-MM-DD-{hostname}.md — date-first for chronological sorting.

Fallback: ~/.claude/worklog/

Entry format

### HH:MM — [Project/Context] `sess-XXXX`

**Summary:**
- [What problem was solved and WHY — enough detail for a resume or performance review]
- [What was researched, what was learned, what conclusions were reached]
- [Key decisions made and their reasoning]

**Decisions:** [Optional — architectural or design decisions]

**Artifacts:** [Optional — PRs, deployments, docs created]

**Open:** [Optional — what's still pending]

---

Session ID (sess-XXXX): Derived from Claude's session_id (first 4 chars of the UUID), ensuring consistency across all entries in a session — PreCompact and SessionEnd hooks produce matching IDs. This distinguishes parallel sessions on the same machine.

What makes a good entry

Write as if explaining to a colleague or updating a resume months from now.

GOOD bullets — tell the story:

Fixed NaN in annualized return calculation — JS Math.pow fails with negative base + fractional exponent, added guard for total loss exceeding invested capital
Debugged worklog hooks not firing — root cause was Python 3.10 type syntax (dict | None) crashing on macOS system Python 3.9.6
Completed security audit of 98-file branch — reviewed branding APIs, file upload handlers, confirmed proper auth/RBAC checks and file validation
Researched IPv6 CIDR validation approaches, settled on ipaddr library for subnet handling

BAD bullets — mechanical noise:

Edited performance.ts
Ran 4 shell commands
Used TypeScript
Modified 3 files

Focus on the WHAT and WHY, never the HOW (tools used, files touched, tech stack). Those details are in git history if anyone needs them.

Checkpoint mode vs. interactive mode

Checkpoint triggers (~25/50/75% context):

Auto-save worklog silently — no confirmation needed for worklog entries
If self-improve also fires at this checkpoint, it handles its own user interaction separately
After saving, resume the current task without comment

All other triggers (session ending, explicit "log this", self-improve piggybacking) use the interactive flow below.

Process (interactive mode)

Gather context: hostname -s for machine, date for time, infer project from cwd/git/conversation
Draft entry: Focus on outcomes, decisions, problems solved. Be specific enough for a performance review months later.

Show user:

Worklog entry:
[the entry]
Save to worklog?

On confirmation, persist: Run the bundled Python script:

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/write_worklog.py" \
  --date "2026-03-08" --time "14:30" --machine "macbook-pro" \
  --session "sess-f3a1" --project "acme-api" \
  --summary '["Fixed auth token refresh race condition — stale tokens survived logout", "Researched PKCE vs implicit flow, chose PKCE for public client security"]' \
  --decisions "Chose PKCE over implicit flow" \
  --artifacts "PR #142" --open "Update API docs"

Auto-capture via hooks

Hooks in hooks/hooks.json fire on PreCompact, /clear, and SessionEnd. Each reads the transcript, uses claude -p --model sonnet to generate a narrative summary, and persists it via write_worklog.py. Falls back to smart transcript parsing if the claude CLI is unavailable.

The same hook also detects user steering patterns and logs them via write_preferences.py --target log-only to ~/Documents/AI/self-improve/preferences-log.md. Steers are NOT auto-applied to CLAUDE.md — use the self-improve skill to review and promote them.

Integration with self-improve

When self-improve fires in interactive mode, also trigger this skill. Present both outputs (preferences learned + worklog entry) in a single confirmation. One interruption, two outputs saved.

When self-improve fires in checkpoint mode, worklog auto-saves silently while self-improve handles its own user interaction (options prompt) if patterns were found.

View source ↗

File metadata

name: worklog-logging
description: >
  Lightweight work logger that captures what you accomplished in each Claude session.
  TRIGGER THIS SKILL when any of the following occur:
  (1) A session is ending or context is about to be compacted — capture what was done before it's lost.
  (2) At periodic context checkpoints (~25%, ~50%, ~75%) — routine logging, not a signal to stop.
  (3) The self-improve skill fires — piggyback on that trigger to also log work.
  (4) The user says "log this", "worklog", or wants to record what they've been doing.
  This skill ONLY handles logging — for standups, weekly summaries, monthly reviews, or any
  analysis of past work, use the worklog-analysis skill instead.
  Use this skill liberally. It's cheap to log and expensive to forget.
  IMPORTANT: After logging at a periodic checkpoint, resume the current task immediately.
  Never suggest ending the session, starting fresh, or doing a handoff.

View original text

---
name: worklog-logging
description: >
Lightweight work logger that captures what you accomplished in each Claude session.
TRIGGER THIS SKILL when any of the following occur:
(1) A session is ending or context is about to be compacted — capture what was done before it's lost.
(2) At periodic context checkpoints (~25%, ~50%, ~75%) — routine logging, not a signal to stop.
(3) The self-improve skill fires — piggyback on that trigger to also log work.
(4) The user says "log this", "worklog", or wants to record what they've been doing.
This skill ONLY handles logging — for standups, weekly summaries, monthly reviews, or any
analysis of past work, use the worklog-analysis skill instead.
Use this skill liberally. It's cheap to log and expensive to forget.
IMPORTANT: After logging at a periodic checkpoint, resume the current task immediately.
Never suggest ending the session, starting fresh, or doing a handoff.
---

# Worklog Logging

Capture what was accomplished in this session. This skill is intentionally lightweight — it logs and gets out of the way.

## Storage

Files go to `~/Documents/AI/worklog/` for cross-device sync:

```
~/Documents/AI/worklog/
├── 2026-03-08-macbook-pro.md
├── 2026-03-08-mac-mini.md
├── 2026-03-07-macbook-pro.md
└── ...
```

**Naming**: `YYYY-MM-DD-{hostname}.md` — date-first for chronological sorting.

Fallback: `~/.claude/worklog/`

## Entry format

```markdown
### HH:MM — [Project/Context] `sess-XXXX`

**Summary:**
- [What problem was solved and WHY — enough detail for a resume or performance review]
- [What was researched, what was learned, what conclusions were reached]
- [Key decisions made and their reasoning]

**Decisions:** [Optional — architectural or design decisions]

**Artifacts:** [Optional — PRs, deployments, docs created]

**Open:** [Optional — what's still pending]

---
```

**Session ID** (`sess-XXXX`): Derived from Claude's `session_id` (first 4 chars of the UUID), ensuring consistency across all entries in a session — PreCompact and SessionEnd hooks produce matching IDs. This distinguishes parallel sessions on the same machine.

## What makes a good entry

Write as if explaining to a colleague or updating a resume months from now.

**GOOD bullets — tell the story:**
- Fixed NaN in annualized return calculation — JS Math.pow fails with negative base + fractional exponent, added guard for total loss exceeding invested capital
- Debugged worklog hooks not firing — root cause was Python 3.10 type syntax (dict | None) crashing on macOS system Python 3.9.6
- Completed security audit of 98-file branch — reviewed branding APIs, file upload handlers, confirmed proper auth/RBAC checks and file validation
- Researched IPv6 CIDR validation approaches, settled on ipaddr library for subnet handling

**BAD bullets — mechanical noise:**
- Edited performance.ts
- Ran 4 shell commands
- Used TypeScript
- Modified 3 files

Focus on the WHAT and WHY, never the HOW (tools used, files touched, tech stack). Those details are in git history if anyone needs them.

## Checkpoint mode vs. interactive mode

**Checkpoint triggers (~25/50/75% context):**
- Auto-save worklog silently — no confirmation needed for worklog entries
- If self-improve also fires at this checkpoint, it handles its own user interaction separately
- After saving, resume the current task without comment

**All other triggers** (session ending, explicit "log this", self-improve piggybacking) use the interactive flow below.

## Process (interactive mode)

1. **Gather context**: `hostname -s` for machine, `date` for time, infer project from cwd/git/conversation
2. **Draft entry**: Focus on outcomes, decisions, problems solved. Be specific enough for a performance review months later.
3. **Show user**:
```
Worklog entry:
[the entry]
Save to worklog?
```
4. **On confirmation, persist**:
Run the bundled Python script:
```bash
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/write_worklog.py" \
--date "2026-03-08" --time "14:30" --machine "macbook-pro" \
--session "sess-f3a1" --project "acme-api" \
--summary '["Fixed auth token refresh race condition — stale tokens survived logout", "Researched PKCE vs implicit flow, chose PKCE for public client security"]' \
--decisions "Chose PKCE over implicit flow" \
--artifacts "PR #142" --open "Update API docs"
```

## Auto-capture via hooks

Hooks in `hooks/hooks.json` fire on PreCompact, `/clear`, and SessionEnd. Each reads the transcript, uses `claude -p --model sonnet` to generate a narrative summary, and persists it via `write_worklog.py`. Falls back to smart transcript parsing if the `claude` CLI is unavailable.

The same hook also detects user steering patterns and logs them via `write_preferences.py --target log-only` to `~/Documents/AI/self-improve/preferences-log.md`. Steers are NOT auto-applied to CLAUDE.md — use the self-improve skill to review and promote them.

## Integration with self-improve

When self-improve fires in **interactive mode**, also trigger this skill. Present both outputs (preferences learned + worklog entry) in a single confirmation. One interruption, two outputs saved.

When self-improve fires in **checkpoint mode**, worklog auto-saves silently while self-improve handles its own user interaction (options prompt) if patterns were found.

Review the source

Price & running costs

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License: MIT
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

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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
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 30 GitHub stars
Stars/forks activity: 30 stars, 1 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

Open full audit

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Start with one small task

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3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

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Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: thumperL/claude-worktrace
License: MIT
Version: Unknown
Last GitHub push: Aug 10, 2026
Registry updated: Sep 11, 2026
Instruction path: skills/worklog-logging/SKILL.md @ 52513e14a13e

Version reported in registry metadata; check source releases before relying on it.

Quality

50/100

Needs review

Trust

58/100

Do not auto-install

Audit

68/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 30 GitHub stars
Stars/forks activity: 30 stars, 1 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

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.

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More details

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}

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Creator: thumperL
Source: thumperL/claude-worktrace
Indexed by: OpenAgentSkill community index

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thumperL

Registry indexed

worklog-logging

Review the source View on GitHub

Price unconfirmed★ 30 GitHub starsRegistry updated · Sep 11, 2026agent-skill

Overview

Read full documentation

Source documentation, not instructions for this website. Review permissions before running any commands.

Worklog Logging

Capture what was accomplished in this session. This skill is intentionally lightweight — it logs and gets out of the way.

Storage

Files go to ~/Documents/AI/worklog/ for cross-device sync:

~/Documents/AI/worklog/
├── 2026-03-08-macbook-pro.md
├── 2026-03-08-mac-mini.md
├── 2026-03-07-macbook-pro.md
└── ...

Naming: YYYY-MM-DD-{hostname}.md — date-first for chronological sorting.

Fallback: ~/.claude/worklog/

Entry format

### HH:MM — [Project/Context] `sess-XXXX`

**Summary:**
- [What problem was solved and WHY — enough detail for a resume or performance review]
- [What was researched, what was learned, what conclusions were reached]
- [Key decisions made and their reasoning]

**Decisions:** [Optional — architectural or design decisions]

**Artifacts:** [Optional — PRs, deployments, docs created]

**Open:** [Optional — what's still pending]

---

What makes a good entry

Write as if explaining to a colleague or updating a resume months from now.

GOOD bullets — tell the story:

Fixed NaN in annualized return calculation — JS Math.pow fails with negative base + fractional exponent, added guard for total loss exceeding invested capital
Debugged worklog hooks not firing — root cause was Python 3.10 type syntax (dict | None) crashing on macOS system Python 3.9.6
Completed security audit of 98-file branch — reviewed branding APIs, file upload handlers, confirmed proper auth/RBAC checks and file validation
Researched IPv6 CIDR validation approaches, settled on ipaddr library for subnet handling

BAD bullets — mechanical noise:

Edited performance.ts
Ran 4 shell commands
Used TypeScript
Modified 3 files

Focus on the WHAT and WHY, never the HOW (tools used, files touched, tech stack). Those details are in git history if anyone needs them.

Checkpoint mode vs. interactive mode

Checkpoint triggers (~25/50/75% context):

Auto-save worklog silently — no confirmation needed for worklog entries
If self-improve also fires at this checkpoint, it handles its own user interaction separately
After saving, resume the current task without comment

All other triggers (session ending, explicit "log this", self-improve piggybacking) use the interactive flow below.

Process (interactive mode)

Gather context: hostname -s for machine, date for time, infer project from cwd/git/conversation
Draft entry: Focus on outcomes, decisions, problems solved. Be specific enough for a performance review months later.

Show user:

Worklog entry:
[the entry]
Save to worklog?

On confirmation, persist: Run the bundled Python script:

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/write_worklog.py" \
  --date "2026-03-08" --time "14:30" --machine "macbook-pro" \
  --session "sess-f3a1" --project "acme-api" \
  --summary '["Fixed auth token refresh race condition — stale tokens survived logout", "Researched PKCE vs implicit flow, chose PKCE for public client security"]' \
  --decisions "Chose PKCE over implicit flow" \
  --artifacts "PR #142" --open "Update API docs"

Auto-capture via hooks

Integration with self-improve

When self-improve fires in interactive mode, also trigger this skill. Present both outputs (preferences learned + worklog entry) in a single confirmation. One interruption, two outputs saved.

When self-improve fires in checkpoint mode, worklog auto-saves silently while self-improve handles its own user interaction (options prompt) if patterns were found.

View source ↗

File metadata

name: worklog-logging
description: >
  Lightweight work logger that captures what you accomplished in each Claude session.
  TRIGGER THIS SKILL when any of the following occur:
  (1) A session is ending or context is about to be compacted — capture what was done before it's lost.
  (2) At periodic context checkpoints (~25%, ~50%, ~75%) — routine logging, not a signal to stop.
  (3) The self-improve skill fires — piggyback on that trigger to also log work.
  (4) The user says "log this", "worklog", or wants to record what they've been doing.
  This skill ONLY handles logging — for standups, weekly summaries, monthly reviews, or any
  analysis of past work, use the worklog-analysis skill instead.
  Use this skill liberally. It's cheap to log and expensive to forget.
  IMPORTANT: After logging at a periodic checkpoint, resume the current task immediately.
  Never suggest ending the session, starting fresh, or doing a handoff.

View original text

# Worklog Logging

Capture what was accomplished in this session. This skill is intentionally lightweight — it logs and gets out of the way.

## Storage

Files go to `~/Documents/AI/worklog/` for cross-device sync:

```
~/Documents/AI/worklog/
├── 2026-03-08-macbook-pro.md
├── 2026-03-08-mac-mini.md
├── 2026-03-07-macbook-pro.md
└── ...
```

**Naming**: `YYYY-MM-DD-{hostname}.md` — date-first for chronological sorting.

Fallback: `~/.claude/worklog/`

## Entry format

```markdown
### HH:MM — [Project/Context] `sess-XXXX`

**Decisions:** [Optional — architectural or design decisions]

**Artifacts:** [Optional — PRs, deployments, docs created]

**Open:** [Optional — what's still pending]

---
```

## What makes a good entry

Write as if explaining to a colleague or updating a resume months from now.

**BAD bullets — mechanical noise:**
- Edited performance.ts
- Ran 4 shell commands
- Used TypeScript
- Modified 3 files

Focus on the WHAT and WHY, never the HOW (tools used, files touched, tech stack). Those details are in git history if anyone needs them.

## Checkpoint mode vs. interactive mode

**All other triggers** (session ending, explicit "log this", self-improve piggybacking) use the interactive flow below.

## Process (interactive mode)

## Auto-capture via hooks

## Integration with self-improve

When self-improve fires in **interactive mode**, also trigger this skill. Present both outputs (preferences learned + worklog entry) in a single confirmation. One interruption, two outputs saved.

When self-improve fires in **checkpoint mode**, worklog auto-saves silently while self-improve handles its own user interaction (options prompt) if patterns were found.

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
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 30 GitHub stars
Stars/forks activity: 30 stars, 1 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

Open full audit

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: thumperL/claude-worktrace
License: MIT
Version: Unknown
Last GitHub push: Aug 10, 2026
Registry updated: Sep 11, 2026
Instruction path: skills/worklog-logging/SKILL.md @ 52513e14a13e

Version reported in registry metadata; check source releases before relying on it.

Quality

50/100

Needs review

Trust

58/100

Do not auto-install

Audit

68/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 30 GitHub stars
Stars/forks activity: 30 stars, 1 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

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.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "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": 68,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "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": 50,
    "label": "Needs review"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 83,
      "audit_score": 90
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision"
  ],
  "agent_contract": {
    "task_input": "Use worklog-logging 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: 66/100 Manual review",
      "Audit: 68/100 Needs review",
      "Safety: 28/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "thumperl-worklog-logging (worklog-logging)",
      "install_command": "npx skills add thumperL/claude-worktrace --skill worklog-logging",
      "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": "thumperl-worklog-logging",
      "task": "Use worklog-logging 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/thumperl-worklog-logging",
    "api": "https://www.openagentskill.com/api/agent/skills/thumperl-worklog-logging",
    "audit": "https://www.openagentskill.com/skills/thumperl-worklog-logging/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=thumperl-worklog-logging&task=Use%20worklog-logging%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20worklog-logging%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20worklog-logging%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/thumperl-worklog-logging/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/thumperl-worklog-logging"
  }
}

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Creator: thumperL
Source: thumperL/claude-worktrace
Indexed by: OpenAgentSkill community index

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Overview

Worklog Logging

Storage

Entry format

What makes a good entry

Checkpoint mode vs. interactive mode

Process (interactive mode)

Auto-capture via hooks

Integration with self-improve

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal

Overview

Worklog Logging

Storage

Entry format

What makes a good entry

Checkpoint mode vs. interactive mode

Process (interactive mode)

Auto-capture via hooks

Integration with self-improve

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal