analyze-trajectory

分类: 工具与效率 | 上传者: yologdev | 下载: 0 | 版本: v1.0（最新）
通过分派子代理来消化 CI 日志，而不会使主上下文膨胀，从而诊断重复出现的故障（STUCK 任务、集群 CI 错误、频繁恢复）。返回一项根本原因诊断。
更新日志: Source: GitHub https://github.com/yologdev/yoyo-evolve
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SKILL.md

---
name: analyze-trajectory
description: Diagnose a recurring failure (STUCK task, clustered CI error, frequent reverts) by dispatching sub-agents to digest CI logs without bloating main context. Returns one root-cause diagnosis.
tools: [bash, read_file, sub_agent, shared_state]
core: true
origin: creator
---

# Analyze Trajectory

You are doing a **deep dive** into a recurring failure pattern. The harness's pre-computed `YOUR TRAJECTORY` block surfaces *that* something is recurring; this skill helps you understand *why* and produce a focused diagnosis.

This skill exists because raw GitHub Actions logs are too large and noisy to digest in your main context window. The pattern (Recursive Language Model — see Reithan's reference in issue #226) is: keep your root context small, dispatch a sub-agent to read the raw logs, and have the sub-agent return a 1-3 sentence summary. Recurse if the summary surfaces a deeper question.

## When to use

Trigger this skill when ANY of these hold:

- `YOUR TRAJECTORY` flagged a `STUCK` task (≥3 attempts in window, 0 successes)
- A CI error fingerprint appeared `≥2×` in the recurring-errors section
- Multiple revert commits appeared across recent sessions (the trajectory's "Reverts in window" line shows the count)
- A specific issue (e.g. `#205`) has been mentioned in multiple session journals without resolution

## When NOT to use

- The trajectory looks healthy. Don't spelunk for problems that aren't there — that's just burning sub-agent budget.
- The failure is well-understood already (you already know the cause from journal/learnings). Skip straight to the fix.
- You're inside Phase B (implementation) and the failure is the task you're currently doing — fix it directly, don't recurse.

## Procedure

### 1. Frame the question (single sentence)

Examples of well-framed questions:
- *"Why does the evaluator phase fail with 'AnthropicError: rate_limit_exceeded' on sessions day-53, day-55, and day-56?"*
- *"Why was the task 'Add /fallback flag' reverted on 6 separate sessions? What's the recurring blocker?"*
- *"What does run 4321 look like at the moment of failure?"*

A good question names a specific event (run id, session day, error fingerprint) and what you want to know about it. Don't ask vague questions like *"what's wrong with my trajectory?"*

### 2. Identify the artifact

For each question, pick exactly one artifact to fetch:

- **CI failure** → run id from the trajectory's CI errors section. `gh run view <id> --log-failed` (drop `--repo`; gh auto-detects from the local clone's origin remote, which is the right one)
- **Reverted task** → commit SHA of the revert. `git show <sha>` and the next-newer commit's full diff
- **Session-level wreckage** → audit.jsonl from that session. **Note**: `$YOYO_AUDIT_DIR` is set by the harness ONLY inside `scripts/skill_evolve.sh` (a different invocation than evolve.sh). When loaded inside a normal evolve session, you must fetch the audit-log branch yourself first:
  ```bash
  git fetch --depth 50 origin audit-log:audit-log
  AUDIT_WT=$(mktemp -d)
  git worktree add "$AUDIT_WT" audit-log
  ls "$AUDIT_WT/sessions/" | tail -10
  # ... read what you need ...
  git worktree remove --force "$AUDIT_WT"
  ```

### 3. Decide: direct read or sub-agent?

Estimate the artifact size first:
```bash
gh run view <id> --log-failed 2>/dev/null | wc -c
```

- **< 5KB**: read it directly with `read_file` or `bash`. Skip sub-agent — the cost isn't worth it.
- **≥ 5KB**: dispatch a sub-agent. Don't load raw logs into your main context.

### 3.5. Handle large artifacts (token-aware chunking)

Before dispatching a sub-agent, estimate whether the artifact fits in a single sub-agent's context:

```
estimated_tokens = artifact_bytes / 4
```

**If estimated_tokens ≤ 30,000** (roughly half a sub-agent's context window): proceed to Step 4 as normal — single sub-agent dispatch.

**If estimated_tokens > 30,000**: the artifact is too large for one sub-agent to digest reliably. Split and fan out:

1. **Split into chunks** of ~20,000 tokens (~80,000 bytes) with 2,000-token (~8,000 byte) overlap between consecutive chunks. The overlap ensures error context that spans a chunk boundary isn't lost.

2. **Store each chunk separately** in shared state:
   ```
   shared_state set key="trajectory.run-<id>.chunk-1" value="<first 80KB>"
   shared_state set key="trajectory.run-<id>.chunk-2" value="<next 80KB, starting 8KB before the split>"
   ...
   ```

3. **Dispatch one sub-agent per chunk** with the prompt:
   ```
   You are analyzing CHUNK <N> of <M> from a CI log.

   The chunk is stored in shared state under key "trajectory.run-<id>.chunk-<N>".
   Read it with: shared_state get key="trajectory.run-<id>.chunk-<N>"

   Question: <your single-sentence question from step 1>

   Reply with ONLY a JSON object (no markdown fences, no prose):
   {
     "summary": "1-3 sentences on what this chunk reveals about the failure",
     "key_lines": ["relevant line 1", "relevant line 2"],
     "chunk_relevant": true,
     "confidence": "high|medium|low"
   }

   If this chunk contains no information relevant to the question, set chunk_relevant to false
   and keep summary/key_lines minimal.
   ```

4. **Merge chunk results** — after all chunk sub-agents return, store their combined results in shared state and dispatch one final merge sub-agent:
   ```
   shared_state set key="trajectory.run-<id>.chunk-results" value="<JSON array of chunk responses>"
   ```

   Merge sub-agent prompt:
   ```
   You are merging analyses from <M> chunks of a single CI log.

   The chunk analyses are stored in shared state under key "trajectory.run-<id>.chunk-results".
   Read them with: shared_state get key="trajectory.run-<id>.chunk-results"

   Original question: <your single-sentence question from step 1>

   Synthesize the chunk analyses into a single diagnosis.
   Reply with ONLY a JSON object (no markdown fences, no prose):
   {
     "summary": "1-3 sentences explaining the root cause",
     "key_lines": ["most important line 1", "most important line 2"],
     "deeper_question": null,
     "confidence": "high|medium|low"
   }
   ```

5. The merge sub-agent's response is your diagnosis — validate it using the same JSON contract rules in Step 4.

**Chunking counts toward the recursion cap** (Step 5): each chunk sub-agent is depth 1, the merge sub-agent is depth 1. If chunking used 4 chunk agents + 1 merge agent, you've used 1 of your 3 recursion levels. You can still recurse on a `deeper_question` from the merge result, but be mindful of the budget.

### 4. Dispatch a sub-agent (if needed)

**Store the artifact in shared state first** — don't paste large logs into the sub-agent prompt. Sub-agents automatically have access to the `shared_state` tool and share the same key-value store as their parent.

Use the namespace convention `trajectory.<key>` for all artifacts stored by this skill.

```bash
# 1. Fetch the artifact into a shell variable
LOG=$(gh run view <id> --log-failed 2>/dev/null)

# 2. Store it in shared state (the parent agent calls this directly)
shared_state set key="trajectory.run-<id>" value="$LOG"
```

Then dispatch the sub-agent with a **reference**, not the artifact itself:

```
Question: <your single-sentence question from step 1>

The CI log is stored in shared state under key "trajectory.run-<id>".
Read it with: shared_state get key="trajectory.run-<id>"

Reply with ONLY a JSON object (no markdown fences, no prose) matching this schema:

{
  "summary": "1-3 sentences explaining the root cause, with no surrounding quotes",
  "key_lines": ["file.rs:42:11 borrow of moved value", "AnthropicError: rate_limit_exceeded"],
  "deeper_question": null,
  "confidence": "medium"
}

Field rules:
- summary: free string, 1-3 sentences
- key_lines: array of 1-5 short strings (max 100 chars each) that prove the cause
- deeper_question: JSON null when no follow-up is needed; otherwise a single-sentence string
- confidence: exactly one of "high", "medium", or "low"
```

Sub-agents inherit RTK compression on bash output and directory restrictions, but they do NOT inherit skills. Keep the sub-agent prompt fully self-contained — don't reference other skills. Sub-agents share the parent's `SharedState` store automatically (via `SharedStateTool` wired by `build_sub_agent_tool`).

**Validate the sub-agent response** — after the sub-agent returns, check:

1. **Parse as JSON.** Strip any leading/trailing whitespace and markdown code fences (` ```json ... ``` `) that sub-agents sometimes add despite instructions.
2. **Check required fields.** The parsed object must contain all four keys: `summary` (string), `key_lines` (array of strings), `deeper_question` (string or null), `confidence` (one of `"high"`, `"medium"`, `"low"`).
3. **If valid** → proceed to Step 5 (recursion check).
4. **If invalid** → retry ONCE with this prompt:

```
Your previous response was not valid JSON or was missing required fields.

Please respond with ONLY a JSON object (no markdown, no explanation):
{
  "summary": "1-3 sentences explaining the root cause",
  "key_lines": ["key line 1", "key line 2"],
  "deeper_question": null,
  "confidence": "high|medium|low"
}

Required fields: summary (string), key_lines (array), deeper_question (string or null), confidence ("high"|"medium"|"low").
```

5. **If retry also fails** → fall back gracefully (see below).

**Sub-agent failure fallback** — if the sub-agent (a) errors, (b) returns non-JSON twice (initial + retry), (c) returns truncated JSON that can't be repaired, or (d) is unavailable as a tool:

1. Append the raw response to `memory/learnings.jsonl` as a learning entry with `pattern_key: trajectory.subagent_malformed_response` so we can debug later.
2. Extract whatever text the sub-agent did return and treat it as the `summary` field. Construct a synthetic response: `{"summary": "<raw text, first 500 chars>", "key_lines": [], "deeper_question": null, "confidence": "low"}`.
3. If even the raw text is empty or the sub-agent errored entirely, downgrade to a direct read of the artifact: use `shared_state get key="trajectory.run-<id>"` to retrieve the stored log, then read the last 50-100 lines in your main context.
4. Produce a low-confidence diagnosis from what you can see directly. Skip recursion (no point — sub-agent path is broken).
5. Mark the diagnosis with `confidence: low (sub-agent unavailable)` so downstream decisions know to be cautious.

### 5. Recurse if the sub-agent returns `deeper_question`

If `confidence` is `"low"` AND `deeper_question` is a non-null string (JSON null returns false on this check, but if you see the literal string `"null"` treat it as null too — that's a sub-agent bug worth logging), run another sub-agent dispatch with the narrower question. Reuse the same artifact; the sub-agent will focus differently.

**Hard cap: recursion depth = 3.** That's: initial dispatch → 1st recursion → 2nd recursion. After that, accept whatever you have. The cap is informed by the recursive-LM literature ([RLM blog, alexzhang13.github.io/blog/2025/rlm/](https://alexzhang13.github.io/blog/2025/rlm/)) and prevents runaway agent costs.

If you hit the cap without `confidence == "high"`, that's still a valid outcome — write the diagnosis with whatever clarity you have and flag it as "needs follow-up".

### 6. Aggregate to a single diagnosis

Produce a 3-5 sentence diagnosis paragraph that includes:
- **What recurs**: one-line summary of the pattern
- **Root cause** (or best-guess): from the sub-agent's summary
- **Evidence**: ≤3 specific lines or run IDs
- **Suggested next attempt**: one concrete action (a different approach, a new task, or "log to learnings.jsonl and skip for now")

Write the diagnosis somewhere durable:
- If you're in a normal evolve session and this informed your task choice → cite it in the assessment doc
- If you're investigating a specific issue → comment on the issue with the diagnosis
- Always also append a `learnings.jsonl` entry. The `pattern_key` field (optional in the standard schema, see `skills/communicate/SKILL.md`) takes a kebab-case `<verb>.<object>` value — for trajectory-derived diagnoses, use `pattern_key: trajectory.<short-slug>` (e.g., `trajectory.fallback_provider_stuck`, `trajectory.evaluator_rate_limit`). This lets skill-evolve cluster recurring trajectory findings.

## Pitfalls

- **Don't ask the sub-agent to make decisions.** It summarizes evidence; you decide what to do. Sub-agents in chained recursion can drift if asked to plan.
- **Don't recurse on `confidence: high`.** The whole point is to stop early when you have a clear answer.
- **Don't dump multiple artifacts to one sub-agent.** One artifact per dispatch keeps the sub-agent focused and the JSON output reliable. Store each artifact under a separate `trajectory.<key>` in shared state.
- **Don't forget the recursion cap.** 3 is the hard limit. If you find yourself wanting depth 4, your initial question was probably too vague — go back to step 1.
- **Skills do not chain.** Sub-agents don't load this skill or any other; you must include the question and shared-state key reference in the sub-agent's prompt directly.
- **Don't run this skill inside Phase B (implementation).** That's task-execution time, not introspection time. Save the diagnosis for the next session's Phase A1 (assess).

## Verification

A diagnosis is "good enough" when ALL of:
- It names a concrete file/line/condition (not "something with the API")
- It cites at least one specific run id or commit SHA
- The suggested next attempt is *different* from what's already been tried (otherwise you'll just hit the same wall)
- The total work used ≤3 sub-agent dispatches

If the diagnosis fails any of these, recurse one more time (within the cap) or accept the partial result and document the open question in `learnings.jsonl`.

## What this skill deliberately does NOT do

- **Does not modify code.** Diagnosis is the output. The actual fix is a normal task on a future evolve session — it's better to step away with the diagnosis written down and let the next session's planning agent decide whether to act on it.
- **Does not auto-create issues.** If the diagnosis is worth filing, do it via `communicate` skill in the same session — but it's a separate decision, not part of this skill's procedure.
- **Does not write to `audit-log` branch.** The branch is read-only from this skill's perspective.
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