Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ijust/intent-planner --skill intent-discovergit clone --depth 1 https://github.com/ijust/intent-plannerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ijust/intent-planner/intent-discover)<a href="https://agentmods.dev/skills/ijust/intent-planner/intent-discover"><img src="https://agentmods.dev/badge/skills/ijust/intent-planner/intent-discover/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ijust/intent-planner/intent-discover"><img src="https://agentmods.dev/badge/skills/ijust/intent-planner/intent-discover.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.02897 |
| Opus 5.5 | $0.00021 | $0.01159 |
| Sonnet 5.5 | $0.00011 | $0.00579 |
| Haiku 4.5 | $0.00005 | $0.00290 |
Grade A, and why
intent-discover scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
intent-discover Skill
Core Mission
- Success Criteria:
- The L0–L4 Intent Tree is structured, with canonical (confirmed) and inferred (guessed) separated
- The mode for working out the Intent is recommended/confirmed and recorded in
.intent/mode.local.md(the local canonical source for mode state) - Whether question delegation (designer-questions) is needed is confirmed and recorded in
.intent/mode.local.md(the purpose as well when on; if deferred, it is noted in Open Questions) - Open Questions that the human should review are made explicit
- For divergent work, the AI presents hypotheses, counterexamples, and alternative problem framings as inferred so the human can set decision boundaries in the next compass
- A design principle is handed to downstream phases: explore broadly, have the human confirm decision boundaries in the compass, and keep implementation as bounded autonomy within those confirmed boundaries
- When drift-watch is on, drift-prone-situation pre-check is performed, the matching pattern is named, and it is recorded in drift-log (when off, nothing is done)
- No application code has been changed at all
When designing or changing responsibility/dependency boundaries, read “Compare boundaries” in ../intent-packets/rules/design-boundaries.md before drafting.
Execution Steps
- When drafting, read “Recording the plan” and “Checking meaning” in
../intent-packets/rules/requirement-coverage.md; retain requirement sources and acceptance conditions in the relevant Tree prose.
Step 1: Select the mode
- Read and apply
rules/mode-selection.md. - Check the available modes (
.intent/modes/*.md) and recommend a mode based on the repository situation. - Confirm with the user via
AskUserQuestion(run the recommend→confirm wiring even if standard is the only candidate). - Create an issue directory and record the confirmed result there (A34 — resolving same-machine concurrent collision): on each discover run, create
.intent/discovery/<slug>-<rand>/(an issue directory mirroring packets;<slug>derived from the issue name;<rand>is 4[a-z0-9]chars generated by the shell, no central numbering) and record the confirmed result in itsmode.md(the local canonical source for mode state; not tracked by git). State the issue directory name in the Output so downstream skills inherit it for reading (reader identification). Keep the existing single.intent/mode.local.mdas the backward-compatible legacy read target. Enforcement / Drift-watch (shared policy) stay in.intent/mode.mdand are not touched. See.intent/discovery/README.md. - Create one drafting claim (to tell parallel sessions "this is being drafted" — DR163/INV91): in the same step that creates the issue directory, create
.intent/assignments/discovery-<issue-dir-name>-<session-rand>.md(<session-rand>= 4[a-z0-9]chars generated by the shell). Its frontmatter:phase: drafting/issue_dir: <issue-dir-name>/packet_id: ""(no packet exists yet at drafting time — never fabricate an ID) /declared_at(shelldate) /session/note(optional). Creation is automatic; deletion is manual (deciding that drafting has ended is a human judgment, so a machine never deletes a live claim; INV91). If a claim for the same issue directory already exists, do not create a duplicate (re-runs must not stack claims). Never stop or take over (a claim is read-only guidance and never refuses another session's start). The schema and its rules are governed by.intent/assignments/README.md; the reading contract lives in CONTRACT.md. - Recommend → confirm → record the target format (optional, deferrable): after confirming the mode, when the target format (which exit to take =
cc-sdd/openspec/speckit/to-spec/direct) can be inferred from the case, ask the user to confirm it, and on confirmation record it in theformatline of.intent/mode.local.md. The inference signals are the case type (mode; whether the artifact is code or a document) and the setup markers of the downstream spec tools (whether.kiro/for cc-sdd, a repository-rootopenspec/for OpenSpec, or a repository-root.specify/for Spec Kit exists; read-only observation); the format→exit correspondence and the handling of setup status (set-up tools first; never drop the not-set-up ones from the candidates; never invent a priority among set-up tools) are kept consistent withintent-packets/rules/export-route.md(the exit decision lane, the single source of truth) — do not copy its tables into this file. Choosedirectfor a case implemented directly without a tool (no spec tool launched — e.g. a small-to-medium change that edits code or documents directly); recording it lets/intent-writebackuse that record as the primary signal for target identification (INV34). Follow the same confirmation discipline as mode / designer-questions / purpose: if it cannot be inferred, or the user defers/declines, do not fill it in by guessing — do not record it (continue with it unspecified; the exit decision later falls back to inference; ifdirectis likewise unrecorded, writeback falls back to the 3-condition AND inference). Recording the format is optional; discover continues as before even without it. Only/intent-discoverwrites the format (other skills read it read-only — DR26). - Read
rules/designer-questions.mdand confirm/record question delegation (designer-questions).
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- rules/algo-drift-analysis.md 3.5 KB
- rules/algo-gore-lite.md 5.0 KB
- rules/algo-impact-analysis.md 5.5 KB
- rules/algo-intent-recovery.md 3.6 KB
- rules/design-frame-surfacing.md 5.0 KB
- rules/designer-questions.md 40 KB
- rules/drift-terrain.md 9.0 KB
- rules/mode-selection.md 6.8 KB
- rules/question-pack-surfacing.md 6.9 KB
- rules/role-perspective-review.md 9.5 KB
- rules/screen-design-brief.md 15 KB
- rules/support-routing.md 4.0 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +4 lines 117e57001b51
- 2mo ago First seen · 79 lines · 53 tokens per session scan A 0defa46de0ff
intent-discover is a skill published in the GitHub repository ijust/intent-planner (5 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 2,897 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
repo-hygiene
Use when the scheduled repo-hygiene workflow runs from GitHub Actions (or an operator dry-run) to scan the repository for small, certain docs/test/code hygiene issues and fix them as one batched branch.
test-failure-explorer
Import and query an existing Vitest 3.2 JSON report to explore test failures, file-level errors and original diagnostic evidence.
requirements-writer
Use when converting PM intake into a slice SPEC.md with observable acceptance outcomes, explicit scope, and advisory sibling build dependencies.
refocusing
Use when a long-running agent may have lost the product outcome, when context was compacted, when work has crossed a major boundary, or when a fresh path-based trace is needed before the next consequential action. Not for fresh-session orientation, waking an idle seat, or checkpoints.
orchestration-team
Use when coordinating assignments, selected review boundaries, or blocked work across a rig.
oversight-team
Use when you are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention…