| name | provenance |
| description | Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions. |
Provenance Analyzer
Overview
This skill runs after one or more sessions have completed. It reads recall
events from .evolve/audit.log, locates each session's trajectory, and records
post-hoc influence events for the recalled guidelines.
The mechanical work — reading recall rows, skipping already-assessed pairs,
resolving entity files, and locating trajectories — is done deterministically by
provenance.py candidates. Your job is the judgment: read each candidate and
decide whether the recalled guideline was followed, contradicted, or
not_applicable, then persist that verdict.
Use this skill when you want to compute usage provenance without coupling the
work to the live learn step.
Workflow
Step 1: Get candidates
Run the candidate builder. It emits one JSON object per line (JSONL), one per
unresolved (session_id, entity) recall pair:
python3 ${CLAUDE_PLUGIN_ROOT}/skills/evolve-lite/provenance/scripts/provenance.py candidates
Each candidate looks like:
{
"session_id": "<session-id>",
"entity_id": "<type>/<name>",
"entity_excerpt": "<frontmatter + content of the entity file>",
"trajectory_path": "/path/to/transcript.jsonl",
"trajectory_excerpt": "<head of the trajectory transcript>",
"missing": ["trajectory"]
}
Notes:
entity_id is the path relative to .evolve/entities/ without the .md
suffix, e.g. feedback/foo, guideline/bar, or
subscribed/alice/guideline/baz.- Pairs that already have an
influence row are skipped for you — the builder
reuses the same dedup rule used when influence rows are written. You will
never be handed a duplicate.
- The trajectory locator checks
.evolve/trajectories/ first, then falls back
to the native Claude transcript at
~/.claude/projects/<slug>/<session-id>.jsonl. This means provenance works
even when no .evolve/trajectories/ file was written.
- If an entity file or trajectory cannot be found, the candidate is still
emitted with a
missing: [...] field so the gap is visible. When the
trajectory is missing you usually cannot judge the pair — skip it (do not
guess), unless the entity content alone makes not_applicable certain.
Step 2: Judge each candidate
For each candidate, read entity_excerpt (and open trajectory_path for the
full transcript if the excerpt is not enough). Compare the recalled guideline
against the agent's actual actions in the trajectory and pick exactly one
verdict:
followed — the agent's actual actions are consistent with the guideline.contradicted — the guideline applied, but the agent did the opposite or
repeated the avoidable dead end.not_applicable — the guideline was recalled but did not apply to this
session.
Keep evidence to one short sentence citing a concrete action, tool call, or
absence in the trajectory. This judgment is yours — there is no heuristic
fallback.
Step 3: Record verdicts
Persist each verdict. Either pipe one verdict per call to provenance.py record:
echo '{
"session_id": "<session-id>",
"entity": "<type>/<name>",
"verdict": "followed",
"evidence": "Agent used the saved parser before trying shell fallbacks."
}' | python3 ${CLAUDE_PLUGIN_ROOT}/skills/evolve-lite/provenance/scripts/provenance.py record
…or, to batch many assessments for one session in a single call, pipe to the
underlying writer directly:
echo '{
"session_id": "<session-id>",
"assessments": [
{"entity": "feedback/foo", "verdict": "followed", "evidence": "Agent followed it."},
{"entity": "guideline/bar", "verdict": "not_applicable", "evidence": "Did not apply."}
]
}' | python3 ${CLAUDE_PLUGIN_ROOT}/skills/evolve-lite/provenance/scripts/log_influence.py
Both paths write the identical influence audit row and skip duplicates. The
entity value must match the candidate's entity_id exactly, including any
subscribed/<source>/ prefix.
It is valid to record nothing when recall events exist but no recalled guideline
can be assessed (e.g. every candidate is missing its trajectory).
