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Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Convert a saved trajectory into a reusable agent skill (SKILL.md + supporting scripts) that future agents can invoke to skip rediscovered work. Use when a session captured a non-trivial workflow worth promoting from a free-text guideline to an executable skill.
Convert a saved trajectory into a reusable agent skill (SKILL.md + supporting scripts) that future agents can invoke to skip rediscovered work. Use when a session captured a non-trivial workflow worth promoting from a free-text guideline to an executable skill.
Convert a saved trajectory into a reusable agent skill (SKILL.md + supporting scripts) that future agents can invoke to skip rediscovered work. Use when a session captured a non-trivial workflow worth promoting from a free-text guideline to an executable skill.
Convert a saved trajectory into a reusable agent skill (SKILL.md + supporting scripts) that future agents can invoke to skip rediscovered work. Use when a session captured a non-trivial workflow worth promoting from a free-text guideline to an executable skill.
Mirror a just-saved native memory into the shared evolve store so it becomes shareable and auditable
Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions.
| name | provenance |
| description | Analyze saved trajectories and recall audit events offline to record whether recalled guidelines influenced completed sessions. |
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.
Run the candidate builder. It emits one JSON object per line (JSONL), one per
unresolved (session_id, entity) recall pair:
python3 "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/plugins/evolve-lite/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.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..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.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.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.
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 "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/plugins/evolve-lite/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 "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/plugins/evolve-lite/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).