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 agentmods add skills/ijust/intent-planner/intent-from-specnpx skills add ijust/intent-planner --skill intent-from-specgit 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-from-spec)<a href="https://agentmods.dev/skills/ijust/intent-planner/intent-from-spec"><img src="https://agentmods.dev/badge/skills/ijust/intent-planner/intent-from-spec.svg" alt="Measured on agentmods" 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 | $0.00078 | $0.03640 |
| Opus 5 | $0.00039 | $0.01820 |
| Sonnet 5 | $0.00016 | $0.00728 |
| Haiku 4.5 | $0.00008 | $0.00364 |
Grade A, and why
intent-from-spec 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 3d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
intent-from-spec Skill
Core Mission
- Success Criteria (success if achieved; the extract / check / sort / recap procedure detail is consolidated into Execution Steps, and only the outcomes are declared here):
- Reads the natural-language text (a spec or fragmentary notes) read-only and extracts intent candidates for purpose, outcomes, capabilities, invariants, constraints, anti-direction, and implicit assumptions, marking them all as Assumptions (inferred intent) and never mixing them with canonical (confirmed intent) (Step 1–2. R1.1 / R1.2)
- When the input is fragmentary notes / scribbles / a voice transcript, bundles the fragments by topic (clustering) and sorts them into "decided / undecided" before the 7-category extraction, presenting every bundle and mark with the inferred marker (without over-rewriting the person's words). For organized-document input this pre-stage does not trigger and the existing output structure is unchanged (C49, DR113)
- Limits input to that spec text and does not use source code, execution traces, or test results as extraction input (R1.4). Picks up the spec's technical / security requirements without dropping them as Compass Invariants candidates, limiting their destination to Invariants (reflection into tech.md / design delegated downstream. R1.5 / R1.6)
- When no spec text is provided as input, performs no extraction, asks the user for the spec to ingest, and stops (fail-fast. R1.3)
- Reads the existing rulers (the inspection catalog in
validate-checks.mdand the common-core slots indecision-slots.md) against the spec and enumerates the unfilled items as gaps, showing in an observable form which category / slot each is the silence of, and presenting it as a hypothesis rather than a confirmed defect (R2.1 / R2.2 / R2.3). Does not stop processing while presenting; limits to warnings / awareness (same stance as drift-watch. R2.4) - Qualitatively sorts the extractions / gaps by load-bearing, presenting high distinctly from low. The judgment merely reads and copies the "front-load / defer door" column in
decision-slots.md, and holds no mathematical solver, numeric score, or threshold (R3.1 / R3.2 / R3.3) - Summarizes "what was checked and what was left unfilled" as an omission recap, prompting reconfirmation (R4.1)
- Treats only approved items as candidates for canonical promotion, retains unapproved items as Assumptions without discarding them, performs no auto-reflection, and delegates promotion to the user's manual copy (R4.2 / R4.3 / R4.4)
- Limits output to derived artifacts under
.intent/spec-ingest/, and never modifies any canonical.intent/*.md, application code, or the input spec (read-only. R5.2). Follows theintent-*naming convention and does not modify external spec tools or the kiro-* development environment (R5.6)
What ships with it
4 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.
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.
- 3d ago First seen · 74 lines · 78 tokens per session scan A dd940a04bf47
intent-from-spec is a skill published in the GitHub repository ijust/intent-planner (5 stars, last pushed 3d ago), licensed MIT. It adds 78 tokens to every session and 3,640 once invoked, about $0.0004 per session on Opus 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.
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