Skill Discovery Reference

Use this reference after ../loop-engineering/loop-discovery.md decides Create Skill or Extend Skill, when the user explicitly asks to evaluate known repeated work as a Skill, or when the user asks for a session-informed profile of their software-lifecycle Skill coverage. This reference does not choose among automation, hooks, commands, custom agents, MCP, scripts, rules, or project policy. Return new durable-owner questions to ../loop-engineering/loop-discovery.md. Use Skill Quality Review when the Skill already exists and the question is whether it is well written, safe, efficient, or demonstrably effective.

Contents

• Core Idea
• Executable Onboarding Documentation
• Inputs
• Skill Coverage Check
• Session-Informed Lifecycle Coverage
• Bug-Diagnosis Skill Examples
• Analysis Loop
• Candidate Score
• Skill Package Shape
• Output Template
• Quality Bar
• Platform Notes

Core Idea

A good Skill candidate has repeated or costly demand, or one current bounded SDLC handoff with direct need or behavior evidence. It still needs a stable trigger, reusable input context, repeatable steps, clear output, failure modes, and a validation path. Do not create broad Skills from noisy clusters or from a loop that is already covered by an existing Skill.

Treat a Skill as packaged procedural knowledge for an agent to load at the right time. A Skill can describe how to run a loop, collect evidence, validate output, and hand off to automation, but it is not by itself runtime persistence, tracing, approval handling, background scheduling, or external system access.

Default to read-only analysis. Do not modify selected-platform user-home state, project settings, auth files, model catalogs, plugin caches, transcripts, or repository files unless the user explicitly asks for implementation.

Executable Onboarding Documentation

Treat onboarding as a strong Skill candidate when the repeated work is more than a deterministic install command or static README. A setup Skill can combine README orientation, go.sh-style command sequencing, and agent-run judgment for steps that depend on the current repository, environment, platform, local configuration, generated artifacts, or validation failures.

Evaluate this pattern at three levels:

• codebase-local setup, run, test, and repair workflows, often exposed as a /_setup or project setup Skill
• library or API adoption guidance shipped by maintainers through internal or external Skill registries
• internal platform or design-system onboarding that lowers adoption friction and captures team conventions

Prefer a Skill when onboarding requires state-aware branching, current-context inspection, or cross-tool instructions that a script cannot fully encode. Keep deterministic setup commands in scripts that the Skill references, and return purely scriptable or purely explanatory flows to scripts, README files, or ordinary documentation.

Inputs

Start from the Loop Discovery evidence pack:

• target workspace, platform, and time window
• candidate loop name and repeated intent
• evidence refs such as session ids, reports, artifacts, prompts, commands, or changed files
• proposed decision: Create Skill or Extend Skill
• existing coverage and the missing Skill-owned behavior
• trigger, input context, output, verification, stop condition, and failure modes

For Agent Work Loop review, generated repositoryEvidence.workflowDemandDiagnostics may supply a normalized current handoff or repeated-work lead. Treat /ultraplan, /plan, /spec, /story, and /issue-* user task entries as specification/planning demand, and treat /review as review/acceptance demand. Treat an explicit $spec-review, namespaced lifecycle Skill, or skill:// UI invocation the same way only when its bounded identity maps to a known workflow intent; do not infer lifecycle demand from arbitrary Skill names. Keep the diagnostic read-only and open its bounded Skill capability evidence before choosing a coverage step. A current handoff may enter the coverage ladder directly; a repeated candidate still returns to Loop Discovery for owner selection before Create Skill or Extend Skill. owner-review means the smallest owner is unresolved; it is not by itself approval to create a Skill. Bind a Skill proposal to reviewed owner-selection evidence, and never bypass a matching built-in, configured, observed, or extendable capability.

If no evidence pack exists, run ../loop-engineering/loop-discovery.md first unless the user explicitly asked for the session-informed lifecycle profile below. That profile may return Try platform built-in, Try configured Skill, or Needs more evidence without proving a new loop. Send uncertain owner or new durable-workflow questions back to Loop Discovery before creating or extending a Skill.

Skill Coverage Check

Inspect only enough existing assets to avoid duplicate recommendations:

• repository-local shared and selected-platform Skill stores
• user or plugin Skill packages when the task is about installed/global assets
• command or prompt aliases that invoke an existing Skill
• Skill references, scripts, assets, templates, and validation notes
• mirror metadata such as .agents/skills/*/mirror.json when present

Treat observed Skill use as coverage. Recommend Extend Skill only when the evidence shows repeated manual setup, repair, validation, context collection, or schedule handoff around that Skill.

Session-Informed Lifecycle Coverage

Use this path only when the user asks to inspect their Skill use across projects, find software-lifecycle coverage gaps, or recommend Skills from global session history. It may recommend trying or creating a Skill, but it does not turn every unobserved lifecycle stage into a new durable loop.

Freeze the evidence boundary

1. Run scripts/session-analysis.mjs sources for each requested supported platform with the target workspace first. Add --include-global-capabilities only because this workflow explicitly asks for a user-global profile.
2. Run bounded facets or insights with an explicit --since/--until window when available, --selection stratified, and a stated --limit. Record the eligible/analyzed counts and source gaps; do not silently present a latest-N sample as a complete history.
3. Load the selected Platform Notes before classifying analyzer Skill signals, inferred reads, built-ins, or configured stores. Keep all of them separate from prose mentions.
4. Run scripts/agent-customize/cli.mjs inventory or scripts/coding-agent-practices/inventory.mjs <platform> --workspace <absolute-target-path> --include-user-home --format markdown to collect configured personal, project, plugin, and built-in coverage.
5. Label each claim workspace, user-global, or configured. A capability absent from the bounded sample is not observed, not missing.

Do not expose raw prompts, commands, absolute home paths, or stable session ids in the reader-facing profile. Cite bounded evidence refs in internal or review artifacts only.

Use two coverage axes

Do not rebuild a flat SDLC stage list. Map normalized capability aliases onto two orthogonal axes:

Axis	Coverage families	Common comparison vocabulary
Lifecycle artifacts	brainstorm/research, specify/clarify, plan/tasks, architecture/UX, release/incident, retrospective/standards	Spec Kit, OpenSpec, BMAD, and Agent OS workflows
Engineering disciplines	isolate/worktree, execute/delegate, TDD, systematic debugging, review, verification, branch completion	Superpowers and equivalent discipline Skills

Treat those ecosystems as comparison vocabularies. A session using test-driven-development, /opsx:propose, speckit-plan, or an equivalent team Skill can cover a family without requiring a same-named platform Skill. Look for observable workflow behavior and artifacts, not exact brand strings alone.

Project lifecycle coverage into Agent Work Loop

For the default Agent Work Loop combined review, inspect the practical handoffs from goal/clarification through specification/planning, setup/isolation, debugging/testing/verification, and review/acceptance/branch completion. Do not require a generic coding or implementation Skill: the base agent already owns ordinary code generation, and a Skill is useful only for non-obvious reusable procedure.

Apply the observed -> platform built-in -> configured -> extend -> create -> needs evidence ladder below. Enter it from either repeated or costly demand, or from one bounded current SDLC handoff whose need or behavior is directly observed. Keep confirmed project activation, unscoped observed activation, apparent reads, and configured presence as separate evidence classes; none substitutes for another. Likewise, count built-in coverage only from an inspected, host-scoped built-in catalog entry that matches the normalized workflow; the presence of a slash-command token does not establish coverage. The current-task route is sufficient only when the review can name a reusable non-coding procedure, stable trigger and output, validation or stop boundary, and an inventory-confirmed coverage gap. When at least two comparable episodes support the same repeated opportunity and inspected session plus inventory evidence leaves such a family uncovered, project a supported, deduplicated Learning Capture finding under loop-engineering. Without recurrence, keep the Skill recommendation under the affected current-task dimension. Group lifecycle gaps by causal chain rather than emitting one finding per stage. Not observed, an unavailable source, or a merely configured asset does not by itself prove a gap. Covered or weak-evidence profiles create no finding.

Treat these as positive review leads, not automatic findings:

• observed planning or a non-trivial change with no canonical Spec workflow, when opened Skill coverage has no specification and acceptance-traceability procedure;
• observed branch/worktree setup, rebase or conflict recovery, review handoff, or branch completion with no matching Skill or built-in procedure.

The missing Spec document alone is not a Skill, and a lone git status, commit, or other ordinary Git command is not a Git workflow. Recommend a new Skill only for the smallest reusable procedure behind the evidenced handoff; never recommend a generic git, coding, or whole-SDLC Skill.

Select coverage before creation

Use this priority order for every coverage family:

1. Covered — observed: keep the observed Skill or equivalent platform capability and cite its evidence.
2. Try platform built-in: when a matching built-in is available but no similar workflow was observed, recommend one concrete trial and its success check.
3. Try configured Skill: when no platform built-in matches but an installed personal/project/plugin Skill already covers the family, recommend a bounded trial before declaring a gap.
4. Extend existing Skill: when personal/project/plugin coverage is partial, name the exact Skill and missing trigger, step, output, or validation.
5. Create Skill: when no built-in or installed Skill covers a stable Skill-shaped playbook, use the selected platform's supported creation path instead of inventing a broad lifecycle package.
6. Needs more evidence: use this when the sample, demand, workflow shape, or validation path is too weak.

Customization Checkup may pass an advisory capabilityRecommendation with lifecycle, discipline, evidenceState, nextStep, and handoff. Repeated friction still routes through Repeated Friction Triage and Loop Discovery. A current-task lifecycle review may select Skill ownership directly when the bounded procedure-demand and coverage conditions above are satisfied. Re-run this ladder against current observed, built-in, and configured coverage; do not treat the handoff field as authorization to create or install anything.

Create the Skill when authorized

Keep the default profile read-only. If the user only asks for analysis, return a ready-to-use creation handoff containing the trigger, target workflow, required context, output, validation, and one failure boundary.

If the user explicitly asks to create the recommended Skill, use the selected platform's supported creation workflow and proceed without asking for another generic approval. Use personal scope for a cross-project user workflow and project scope for repository-specific conventions; ask once only when that boundary is genuinely ambiguous. Use the host-native personal/project store reported by inventory.

Create the smallest useful package. Give the Skill a narrow verb-led name and a description that states the triggering condition or state transition. Keep SKILL.md navigation-sized, add scripts only for repeated deterministic work, then validate discovery and run one representative dry-run or task check. Never overwrite an existing or built-in Skill silently.

Bug-Diagnosis Skill Examples

Use bug-topic session evidence only as a lead. For a session-derived repeated opportunity, require at least two distinct comparable Task Episodes with the same reusable reproduction or diagnosis need. Bug/error keywords, issue counts, or repeated attempts inside one Episode do not establish recurrence.

Do not select a frontend or backend example from Session Evidence alone. Join its Task Episodes or Insight Ledger with inspected Project Evidence using the matching project overlay, then use Agent Customize evidence to check existing coverage. Only the lead may route the supported candidate through Loop Discovery and the coverage ladder above.

Supported procedure shape	Joined evidence	Copy/adapt example
Browser or UI reproduction	GitHub Issue, Jira, Aone, or exported screenshots/video/comments plus a project-owned browser, component, E2E, WebView, IDE, or desktop route	reproduce-frontend-bug
Backend correlated diagnosis	Issue evidence plus a project-owned service/test/job route, readable logs or traces, and a stable request/trace/job/run id	diagnose-backend-bug

These directories are example package shapes, not built-ins, configured or observed coverage, creation authorization, or a universal issue-solving Skill. Compose any later authorized repair, validation, PR/MR, and review through the Goal Completion pattern.

Analysis Loop

1. Confirm either a Loop Discovery decision of Create Skill or Extend Skill, or the explicit session-informed lifecycle-profile mode above. In profile mode, apply its evidence ladder first and enter creation only when demand, stability, Skill fit, and validation make the boundary concrete. Demand may be repeated/costly, or one directly evidenced current SDLC handoff with no matching coverage. If the candidate is an automation, hook, script, rule, custom agent, command, or MCP-backed loop without a reusable how-to playbook, return it to ../loop-engineering/loop-discovery.md.
2. Name the Skill boundary:
   • trigger
   • user intent
   • required context
   • steps
   • output artifact or answer shape
   • validation commands or checks
   • failure modes and handoff points
   • runtime dependencies the Skill must not pretend to own: state store, trace/log sink, approval surface, scheduler, MCP server, or CI system
3. Check whether an existing Skill already covers the boundary.
4. Classify the Skill action:
   • Covered by Skill: no new Skill work; cite the existing Skill.
   • Extend Skill: add or adjust instructions, references, scripts, assets, examples, or validation guidance in the existing Skill.
   • Create Skill: create a new Skill only when no existing Skill owns the reusable playbook.
   • Not a Skill: return to Loop Discovery with the reason.
   • Needs more evidence: name the missing session, report, artifact, or validation proof.
5. Keep SKILL.md navigation-sized. Move detailed examples, host-specific rules, long checklists, and platform inventories into references, scripts, assets, or templates.

Candidate Score

Use this compact scoring table before recommending Skill work:

Field	Good signal	Weak signal
Demand	Seen repeatedly/costly, or one current SDLC handoff has direct need or behavior evidence and no matching coverage	One isolated request or tool invocation with no reusable procedure
Skill fit	Reusable how-to workflow with judgment	Deterministic transform or policy check
Onboarding fit	README plus setup script plus environment-aware agent judgment	Single command or static setup notes
Stability	Same trigger, inputs, steps, and output shape	Different goal each time
Validation	Clear test, command, review, or artifact check	Success depends only on taste
Coverage	Missing or incomplete existing Skill coverage	Existing Skill already handles it
Scope	Narrow workflow a first SKILL.md can own	Whole product area or vague theme
Evidence	Session/report/repo facts align	Only cache names, counts, or guesses

For medium-confidence items, output needs more evidence with the exact missing proof.

Skill Package Shape

When the decision is Create Skill, define the smallest useful package:

skills/<skill-name>/
  SKILL.md              # trigger, boundary, steps, validation, output
  references/           # optional long guidance
  scripts/              # optional deterministic helpers
  assets/               # optional reusable templates or examples

When the decision is Extend Skill, name the exact existing file and the smallest change type:

• trigger or description update
• reference link
• validation rule
• script or asset handoff
• example artifact
• host-specific boundary note

Do not add scripts merely to prove a Skill exists. Add scripts only when the workflow has deterministic extraction, validation, formatting, or packaging that the agent would otherwise repeat by hand.

If the loop needs resumable state, human approval, background execution, trace/span export, external writes, or multi-run evaluation storage, keep those contracts in the selected runtime owner and have the Skill reference them explicitly instead of embedding vague "agent will remember" instructions. Use ../loop-engineering/loop-primitives.md, ../loop-engineering/automation-readiness.md, or ../loop-engineering/loop-state-ledger.md for those supporting contracts after Loop Discovery selects them.

Output Template

### Skill discovery
- **Target repo**: ...
- **Loop candidate**: ...
- **Evidence refs**: ...
- **Mode**: read-only | creation authorized

### Lifecycle coverage
| Axis | Capability | Observed use | Configured or built-in | Gap confidence | Next action |
|---|---|---|---|---|---|
| Artifact/Discipline | ... | workspace/user-global/not observed | ... | high/medium/low | Keep/Try built-in/Try configured/Extend/Create/Needs evidence |

| Candidate | Evidence | Existing coverage | Skill action | Decision |
|---|---|---|---|---|
| ... | session/report/repo refs | covered/missing/partial | Covered/Create/Extend/Not a Skill | ... |

### Create Skill
1. ...

### Extend Skill
1. ...

### Not a Skill
1. ...

### Needs more evidence
1. ...

### Safe next validation

Describe the smallest next validation step in prose, not as a hard-coded CLI command.

Quality Bar

• Cite concrete Skill files, repository paths, session ids, reports, or artifacts.
• Keep explicit activation, inferred reads, configured assets, built-ins, and commands as separate evidence classes.
• Prefer a matching platform built-in or built-in command before proposing a new user Skill; never present configured project or personal stores as product built-ins.
• Keep Skill recommendations narrow enough that the first SKILL.md can be useful without scripts.
• For onboarding Skills, separate agent-owned judgment from deterministic setup commands and cite the README, scripts, platform docs, or registry source they complement.
• Separate Skill coverage from loop ownership: automation, hooks, scripts, commands, agents, MCP, and rules are valid loop owners but are not Skills.
• Separate procedural guidance from runtime guarantees: a Skill may instruct an agent to use a session, trace, approval, schedule, or MCP-backed action, but another owner must provide that capability.
• Prefer extending an existing Skill when the evidence shows partial coverage.
• Create a standalone platform Skill only when the workflow is reusable outside one project-specific rule or command.
• Treat missing Spec workflow and bounded Git lifecycle work as review leads; reject document absence alone, ordinary Git use, and generic tool-shaped Skills.

Platform Notes

Load only the selected platform note after the shared evidence and coverage rules identify a Skill-shaped gap. These sections own analyzer semantics, built-in precedence, native creation paths, and host-local stores.

Qoder

Read Qoder topSkills as observed Skill activation. Prove the current product surface from host inventory. In a Qoder source checkout, the embedded built-in owner is chat/agents/tool/skill/builtin_skills/ plus its loader, not repository-local .qoder/skills or .agents/skills.

• ~/.qoder/skills and ~/.agents/skills are personal stores; <repo>/.qoder/skills and <repo>/.agents/skills are project stores. They can supplement the product surface, but they are not Qoder built-ins. The current loader gives an embedded built-in precedence over a same-named personal or project Skill; do not promise a same-name override.
• Globally visible built-in Skills currently include create-skill, create-subagent, canvas, and vercel-deploy.
• create-skill-ui is available only in Quest Agent mode, and schedule only in Quest sessions. State that availability instead of recommending them as universal Skills.
• front-design and wiki-plan-gen are private built-in-agent Skills. Route to the owning built-in agent when available; do not tell the user to invoke them as global Skills.
• /ultra-review is a Qoder built-in command that dispatches three independent review perspectives. Count it as observed review coverage when session evidence shows use, but do not call it a SKILL.md or create a duplicate review Skill unless the user needs a distinct reusable review policy.

Specialize the shared coverage ladder as follows:

1. Covered — observed: keep the observed Skill or equivalent Qoder capability and cite its evidence.
2. Try Qoder built-in: when a matching built-in is available but no similar workflow was observed, recommend one concrete trial and its success check.
3. Try configured Skill: when no Qoder built-in matches but an installed personal/project/plugin Skill already covers the family, recommend a bounded trial before declaring a gap.
4. Extend existing Skill: name the exact partial Skill and missing trigger, step, output, or validation.
5. Create with create-skill: when no built-in or installed Skill covers a stable Skill-shaped playbook, use Qoder's built-in create-skill.
6. Needs more evidence: keep the recommendation read-only when the sample, demand, workflow shape, or validation path is too weak.

Typical Qoder-first moves include create-subagent for a reusable isolated specialist, /ultra-review for deep multi-perspective review, vercel-deploy for a matching web-delivery path, canvas for a durable visual artifact, schedule for an eligible Quest follow-up, and create-skill for the remaining proven playbook. Do not force these onto unrelated lifecycle gaps.

Use the Qoder Platform Reference for the configured feature taxonomy, file locations, and official examples. Treat product/runtime descriptions as availability evidence only: provider-context advertisement may make a context-gated capability available, explicit activation proves use, and task-linked outcome evidence is still required before calling it effective.

When inventory or the /skill menu shows only 0-2 relevant described Skills and the repeated workflow has no matching Skill owner, make the safe next step a Qoder-native /create-skill <workflow description> suggestion. Keep the description concrete: trigger, target workflow, required context, output, validation, and one failure boundary.

If the user explicitly asks to create the recommended Skill, invoke the Qoder built-in create-skill. Use the host-native personal/project store reported by inventory and never overwrite an existing or built-in Skill silently.

Codex

Read Codex topSkills as observed Skill activation. Read Codex inferredSkillReads only as a Skill file that a command appeared to read, not as confirmed activation. Keep both separate from prose mentions and configured Skill presence.

Inspect only the active Codex project, user, or packaged Skill stores reported by inventory. Do not infer that a configured Skill was activated, or that an apparent file read was a Skill invocation.