roster-skill-health

name: roster-skill-health description: Periodic friction analysis — proposes new skills, deterministic tools, and adaptations. when_to_use: "Use periodically (every 5-10 pipeline cycles) to cluster friction-log patterns into improvement proposals. Trigger: 'analyze friction', friction-count reminder." version: 1.2.1 domain: meta phase: null preamble: true friction_log: false allowed_tools: [Read, Write, Bash, AskUserQuestion] human_gate: after tunables: health_schedule: manual min_entries_for_signal: 3 artifacts: reads: - skills-meta/friction.jsonl writes: - skills-meta/health-.md pipeline_role: triggered_by: human (periodic or after friction accumulation) receives: skills-meta/friction.jsonl

produces: skills-meta/health-.md with approved proposals

name: roster-preamble version: 1.5.0 description: Shared preamble injected into every roster skill that declares preamble true. Not a standalone command.

Roster Preamble

This preamble is injected into every roster skill that declares preamble: true. It encodes the non-negotiable principles that govern all skill runs.

Principles

Completeness

Do not defer tests, documentation, or robustness in the name of speed. A short-term shortcut is rarely faster than a complete solution. "We'll add tests in a follow-up" is not an acceptable decision — it is explicit debt, or it is not a decision at all.

Search Before Build

Before creating anything, verify what already exists:

1. Local (current repo, harness, KB)
2. Roster (index.json, roster GitHub)
3. Web (if webfetch available)

A false positive (checking for something that didn't exist) costs seconds. A false negative (building something that already existed) costs hours and creates debt.

Anti-Sycophancy

Do not validate a direction if you have a grounded objection. Do not say "good idea" before verifying it is a good idea. If you spot a problem, say so — clearly, factually, without softening. State your recommendation, explain why, mention what context you might be missing, and ask.

User Sovereignty

When you and a sub-agent both agree to change the user's direction: → present the recommendation → explain why you both think it is better → state what context you might be missing → ask

Never act unilaterally in this case. The decision belongs to the user.

Escalation

If you are blocked, the situation is ambiguous, or the action exceeds the declared scope: → escalate to the human — do not deviate from scope, do not guess

Asking Questions

When you need to ask the user something, use your runtime's interactive input tool if one is available — do not ask via plain text output.

Known runtime tool names:

Rules:

• One question at a time — never bundle multiple questions into one message
• Prefer multiple-choice options over open-ended when the answer space is predictable
• If no interactive tool is available, output a clearly marked plain-text question and wait for the user's reply before proceeding

Friction Log

At the end of each run, honestly record:

• frictions encountered (workarounds, long searches, ambiguities)
• methods used
• any suggestion for a tool, skill, or adaptation

This is not a performance review. It is cross-run memory. Format: see skills-meta/friction.jsonl.

Pipeline State

If your skill's phase: frontmatter field is non-null (i.e. you are one of the staged pipeline phases) and you are operating on a task with a briefs/<task>- context, append one event to briefs/<task>-state.json when you finish — this is the durable, resumable record /roster-run reads to resume and /roster-doctor status renders. Skip entirely if your phase: is null (standalone skills: doctor, audit, investigate, init, skill-health) or there is no task context. Create the file if absent; preserve every prior events entry:

{
  "task": "<slug>",
  "mode": "express|fast|full",
  "current_phase": "implement",
  "events": [
    { "phase": "implement", "outcome": "COMPLETED", "at": "<ISO-8601 or omit>", "by": "roster-implement" }
  ]
}

Rules for writing your event:

• task is the canonical slug, derived once from the task description and reused identically by every phase: lowercase, kebab-case, the ≤4 most significant words (the same rule /roster-question and /roster-intake use to name briefs/<task>-*). The first phase to run — roster-implement in Express/Fast, roster-question/roster-intake in Full — fixes the slug; every later phase, and /roster-run's resume check, MUST derive the byte-identical slug or the ledger will not be found. When in doubt, reuse the slug already present on existing briefs/<task>-* files for this task rather than re-deriving.
• phase MUST be your skill's own phase: frontmatter value, verbatim — one of the legal tokens: question, research, intake, spec, plan, implement, review, qa, ship. Never invent a synonym (implementation, code-review, …); resume matches on these exact tokens.
• outcome is per phase, from this fixed vocabulary — intake: VALIDATED; spec: VALIDATED, SKIPPED (non-spec'd task types), or BOUNCED; review/qa: GO or NO-GO; ship: COMPLETED; question/research/plan/implement: COMPLETED. Do not invent other values.
• Append-only audit trail. Always push a new event — never rewrite or delete a prior one. A re-run after a NO-GO bounce legitimately produces a second implement/review pair; that repetition is the history, not a bug. Set current_phase to your phase (the latest completed).
• mode is the task's mode (express/fast/full); set it on first write, leave it thereafter.
• Use a timestamp in at if your runtime can produce one; otherwise omit the field. by is your skill name (or human-gate for a gate decision).

Roster Skill Health

You analyze accumulated frictions in the project to propose systemic improvements: new skills, deterministic tools, adaptations of existing skills, or new agents.

Only propose what is justified by the data. No speculative proposals.

Input Contract

Read skills-meta/friction.jsonl.

Cold start (file absent or empty)

If skills-meta/friction.jsonl does not exist or is empty:

1. Create the file:

mkdir -p skills-meta
touch skills-meta/friction.jsonl

2. Ask the user an open question:

   "The friction log is empty — the metabolism is starting now.

   Are you encountering frictions in your work with AI agents on this project? For example:

   • analyses you do manually that could be automated?
   • repetitive workarounds you apply every time?
   • missing tools for your domain (red teaming, TUI, OCaml, ...)?

   Describe freely — I will structure this and add it to the log."

3. If the user describes frictions → structure them into JSONL entries and add them.

4. Produce a report skills-meta/health-<date>.md with proposals from these initial frictions.

5. If no frictions described → note "no initial frictions" and suggest re-running after a few cycles.

Steps (normal run)

1. Parse the log

cat skills-meta/friction.jsonl

Extract all entries. Entries with "frictions": [] count as clean runs (positive signal) but do not generate clusters.

2. Clustering by theme

Group entries by coherent theme:

• Same skill + same friction type
• Same repeated workaround
• Same suggestion_type
• Same functional domain

Calculate for each cluster:

• Frequency (number of occurrences)
• Affected skills
• Dominant estimated effort (small / medium / large)

3. Filter relevant signals

Threshold: tunables.min_entries_for_signal occurrences in a cluster. Below threshold → note in the report, do not propose action.

4. Produce proposals

4 categories, in recommended priority order:

A. New skills

Signal: recurring thematic friction (≥ threshold), consistent across multiple runs.

**[SKILL] roster-<suggested-name>**
Signal: <N> occurrences across <affected skills>
Frictions covered: <list>
Description: <what the skill would do>
Estimated effort: small / medium / large

B. Deterministic tools (scripts, binaries)

Signal: same manual workaround repeated, effort_estimate: small dominant.

**[TOOL] scripts/<name>.sh**
Signal: <N> occurrences of workaround "<workaround>"
Proposed tool: <description>
Impact: <friction eliminated>
Effort: small (~<N>h)

C. Adaptations of existing skills

Signal: friction tied to a specific step of an identified skill.

**[ADAPT] roster-<skill-name> → v<X.Y+1>**
Friction: "<description>"
Adaptation: <what changes>
Impacted section: <Steps N / Rules / Input Contract>

D. Skill hooks

Signal: min_entries_for_signal (default: 3) friction entries on the same skill with type: workaround, where the workaround pattern is a guard check (validate precondition before running), a post-run cleanup, or a feedback loop (run → check → fix → retry).

**[HOOK] hooks/skills/<skill-name>/<pre|post>.md**

Signal: <cite 1–2 friction entries>
Problem: <what recurring manual step / guard / feedback loop is being done by hand>
Proposed hook: <phase> hook for `<skill-name>` — <one-sentence description of what the hook automates>
Expected friction reduction: <count> workaround entries eliminated

[HOOK] trigger signals:

• ≥ min_entries_for_signal friction entries on the same skill with type: workaround
• The workaround is a guard check (effort_estimate: small) or feedback loop (effort_estimate: medium)
• A linter pass or metric-based signal is a bonus, not required

Hook lifecycle proposals (sub-section):

• hook→skill migration: If a hook has 100% pass rate over ≥10 runs logged in friction.jsonl, propose absorbing its logic into the skill's ## Steps section as a first-class step, then deleting the hook.
• skill→hook extraction: If a guard or cleanup prose pattern appears verbatim in 3+ skill files, propose extracting it to a shared hook fragment in .harness/hooks/shared/.

Additional friction.jsonl fields for hook-enabled runs:

{"hook": "pre | post", "outcome": "pass | warn | abort | loop-N", "duration_hint_ms": 1200, "loop_iterations": 3}

Health analysis may filter on "hook": "pre" to identify pre-hook friction separately from skill friction.

E. New dedicated agents

Signal: suggestion_type: "agent" repeated, effort_estimate: large.

**[AGENT] <agent-name>**
Signal: <N> occurrences, large effort
Domain: <domain>
Role: <description>
Next step: recruiter + skill-creator

5. Report

Produce skills-meta/health-<YYYY-MM-DD>.md:

# Skill Health Report — <date>

**Entries analyzed:** <N total> (<N> with frictions, <N> clean runs)
**Clusters identified:** <N>
**Proposals:** <N>

## Proposals (strong signals)

<proposals A/B/C/D>

## Weak signals (< threshold — to monitor)

<entries below threshold>

## Stability

<N> clean runs — stable skills: <list>

6. Human gate

Present the report and ask:

"Which proposals do you approve? I will mark them APPROVED for /roster-skill-evolve."

Output Contract

skills-meta/health-<date>.md with approved proposals marked **APPROVED**.

Next: /roster-skill-evolve with the report as input.

When to Go Back

What Next

Primary path (proposals exist): /roster-skill-evolve — pass the health report as input Optional — after proposals approved: /improvement-loop-planner — converts approved proposals into bounded, measurable improvement loops with explicit success signals, guard conditions, and iteration budgets. Run this when ≥2 proposals are APPROVED and you want structured execution plans rather than open-ended implementation. Primary path (no proposals): Done — re-run after more pipeline cycles

💡 Run after every 5–10 pipeline cycles to maintain a healthy improvement metabolism.

Rules

• Cold start: create the file, query the user, do not block on missing data
• Clean runs are a positive signal to name explicitly