capability-ledger
Track DEMONSTRATED problem-solving capability and compute the autonomy it has EARNED — so accumulated competence expands what may be attempted without a human gate and what may be delegated, the way a proven junior is handed bigger problems and a team. Use this when finishing a spike or feature whose result can be judged against a floor, when deciding whether Claude may proceed on a problem class without plan-gate confirmation, or when deciding whether a class may be delegated to a sub-agent. It maintains a repo-local ledger (capability-ledger.jsonl): per problem class, the demonstrated solves with their floor-ratios and domains, a computed maturity (novice, practiced, proven), and the proven playbook for delegation. Authority is the MEASURED outcome against the floor, never self-assessment: license is earned by demonstrated results, climbs slowly across solves and domains, and is demoted instantly on a near-miss. This is how the harness turns learning-from-failure into growing capability and delegation.
What this skill does
# Capability Ledger
The entity layer. The three knowledge skills cache what Claude *knows* (facts,
craft, insight). This tracks what Claude can *do* — demonstrated capability — and
turns it into **earned autonomy**: the license to attempt a bigger thing, skip a
gate, or delegate. It is the difference between a tool that gets faster and an
entity that grows.
## Why earned, never declared (the safety spine)
A system that expands its own authority is gameable if the evidence is
self-assessed. So capability here is computed ONLY from **measured outcomes
against a floor** — the same un-fakeable signal the spike floor gate uses to
prevent false victory now doubles as the promotion oracle. Claude cannot write
"I am good at this"; the ledger counts only spikes/features that actually landed
near their declared floor with a recorded benchmark. Competence is un-fakeable by
the agent itself.
## The unit: a capability (a demonstrated ability to solve a problem CLASS)
`capability-ledger.jsonl`, keyed by `problem_class` (e.g.
`render-perf-optimization`, `idempotent-write-boundary`, `state-machine-design`):
- `solves` — each: `{ floor_ratio, domain, date, benchmark_ref }`. The evidence.
- `maturity` — computed: novice / practiced / proven (never hand-set).
- `playbook` — the proven approach (reframings used, floor to beat, boundaries to
respect) — what makes the capability DELEGABLE, not just a score.
- `last_demotion` — provenance of any instant drop (a near-miss).
## The autonomy ladder (mirrors the enforcement ladder, pointed at license)
- **novice** (0–2 near-floor solves): full gates. Human confirms spec AND result.
The default for every new class.
- **practiced** (>=3 near-floor solves in ONE domain): the PLAN gate may auto-pass
for this class — proceed without plan confirmation; verify gate + human result
review still stand.
- **proven** (>=3 near-floor solves across >=2 domains): licensed to DELEGATE this
class to a sub-agent under the recorded playbook, and to attempt larger blast
radius — because the playbook is demonstrated, not guessed.
No rung auto-passes the spec parse-point (feature-workflow STAGE 0.5): the PLAN
gate is planning *shape*, but intent is not a demonstrated problem class. See
`references/licensing.md`.
"Near-floor" = within the class's agreed ratio of the floor (default <=2x),
proven by a recorded benchmark. No benchmark -> not a solve -> no credit.
## The asymmetry (the guardrail)
License climbs SLOWLY (multiple demonstrated solves, multiple domains) and falls
INSTANTLY (a single near-miss in a class demotes it one rung, recorded in
`last_demotion`). The cost of over-licensing (attempting/delegating beyond real
competence) dwarfs the cost of under-licensing (an extra gate), so the ladder is
deliberately asymmetric. Demotion is real here — unlike the code maturity ladder,
this one must walk back.
## Workflow
- **Record a solve (truth-checked):** `cap_record.py --class <c> --floor-ratio <r>
--domain <d> --benchmark <ref>` after a spike/feature lands. Refuses a solve
with no benchmark ref. Recomputes maturity from the full solve history.
- **Check license (before acting):** `cap_check.py --class <c>` returns the
current maturity and what it licenses (gate auto-pass? delegate?). The feature/
spike loops consult this to decide whether the plan gate may auto-pass.
- **Demote (on a near-miss):** `cap_record.py --class <c> --miss --floor-ratio <r>`
records a failure and drops the rung immediately.
## Composition
- The spike floor gate is the promotion ORACLE — its measured ratio is the solve
evidence. No floor, no capability credit.
- `mental-models` supplies the playbook's reframings; a proven capability's
playbook references the models that made its solves work.
- `knowledge-ratchet` governs nothing here — capability promotion is outcome-driven
and automatic, not friction-driven; this is a distinct, stricter ladder.
- The feature/spike loops READ `cap_check.py` to decide gate auto-pass; they never
WRITE their own license. Read-only consumption keeps the loop from self-licensing.
## The one law here
License reflects DEMONSTRATED outcome, never intention or self-report. A
capability with no benchmarked near-floor solves is novice no matter how confident
the agent feels. Earned, measured, and instantly revocable.
## Files
- `references/licensing.md` — the maturity computation, near-floor rule, demotion.
- `scripts/store.py` — ledger storage port (JSONL).
- `scripts/cap_record.py` — record a truth-checked solve or a demoting miss.
- `scripts/cap_check.py` — read current maturity + what it licenses.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.