Edge Log Proof

VerifiedSafe

Manually logs proof of completed work to a session log file after significant actions. Helps maintain an audit trail when automatic hooks are unavailable, such as after completing a step, modifying files, or running tests.

Sby Skills Guide Bot
ProductivityBeginner
806/2/2026
Codex
#logging#audit-trail#proof-of-work#session-log

Recommended for

Our review

This skill logs proof of completed work to a session log file for audit trail.

Strengths

  • Creates a persistent audit trail
  • Easy to implement with provided templates
  • Integrates with step execution workflow
  • Provides clear evidence for self-assessment

Limitations

  • Manual process relies on developer discipline
  • No automatic detection of actions
  • Logging to file may have privacy implications in shared environments
When to use it

After completing significant actions or steps in a plan to maintain an audit trail.

When not to use it

For trivial actions that don't need recording, or in environments where file-based logging is not appropriate.

Security analysis

Safe
Quality score85/100

The skill only performs local logging via mkdir and echo redirection. It does not exfiltrate data, execute remote code, or disable safety features. It is purely an audit trail mechanism.

No concerns found

Examples

Log step completion
Log the completion of step 3: Add user authentication with JWT. Files modified: src/auth.py, tests/test_auth.py. Outcome: success.
Log file change
Log a file change: edited src/utils.py to fix parsing bug. Reason: the parser was failing on empty input.
Log test run
Log test run: command 'pytest tests/' passed all 42 tests with 0 failures.

name: edge-log description: Log proof of completed work to the session log. Use after completing significant actions to maintain an audit trail.

Log Proof

Since Codex CLI doesn't have automatic PostToolUse hooks, this skill manually logs proof of completed work.

When to Use

Call this skill after:

  • Completing a step in the plan
  • Making significant file changes
  • Running tests
  • Any action that should be recorded for audit

Instructions

1. Gather Proof Information

Collect:

  • What was done: Brief description of the action
  • Files modified: List of changed files
  • Outcome: Success/failure and any relevant output
  • Timestamp: Current time

2. Append to Session Log

Add an entry to .proof/session_log.jsonl:

{
  "timestamp": "2025-01-15T14:30:00Z",
  "type": "manual_log",
  "action": "Completed step 3: Add user authentication",
  "files": ["src/auth.py", "tests/test_auth.py"],
  "outcome": "success",
  "details": "Added JWT-based auth, all tests pass"
}

3. Create Log Directory if Needed

If .proof/ doesn't exist:

mkdir -p .proof

4. Append Entry

echo '{"timestamp":"...","type":"manual_log",...}' >> .proof/session_log.jsonl

Log Entry Format

{
  "timestamp": "<ISO 8601 timestamp>",
  "type": "manual_log",
  "action": "<what was done>",
  "files": ["<list>", "<of>", "<files>"],
  "outcome": "success | failure | partial",
  "details": "<additional context>",
  "step": <step number if applicable>
}

Quick Log Template

For common actions, use these templates:

Step Completion

{
  "timestamp": "<now>",
  "type": "step_complete",
  "step": <N>,
  "description": "<step description>",
  "proof": "<evidence of completion>"
}

File Change

{
  "timestamp": "<now>",
  "type": "file_change",
  "files": ["<path1>", "<path2>"],
  "action": "create | edit | delete",
  "reason": "<why this change was made>"
}

Test Run

{
  "timestamp": "<now>",
  "type": "test_run",
  "command": "<test command>",
  "result": "pass | fail",
  "details": "<N tests, M failures>"
}

Integration with Other Skills

The $edge-step skill should call $edge-log after completing each step.

Example workflow:

  1. $edge-step executes the work
  2. Work completes successfully
  3. $edge-log records the proof
  4. active_context.yaml is updated with step completion

Why This Matters

Without automatic logging:

  • There's no audit trail of what happened
  • Proof claims are unverifiable
  • State changes are invisible

Manual logging provides:

  • Accountability
  • Debugging history
  • Evidence for self-assessment
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