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 HashLips/agent-skills --skill intent-replaygit clone --depth 1 https://github.com/HashLips/agent-skillsWrote 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/hashlips/agent-skills/intent-replay)<a href="https://agentmods.dev/skills/hashlips/agent-skills/intent-replay"><img src="https://agentmods.dev/badge/skills/hashlips/agent-skills/intent-replay/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/hashlips/agent-skills/intent-replay"><img src="https://agentmods.dev/badge/skills/hashlips/agent-skills/intent-replay.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 | $0.00093 | $0.01779 |
| Opus 5.5 | $0.00037 | $0.00712 |
| Sonnet 5.5 | $0.00019 | $0.00356 |
| Haiku 4.5 | $0.00009 | $0.00178 |
Grade A, and why
intent-replay 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 2d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent Replay
Purpose
Maintain one durable, agent-independent source of project intent, decisions, active work, and verified handoffs. The repository owns project context; no agent's conversation is authoritative. Intent Replay coordinates context, not model selection, commits, or deployments.
Non-Negotiable Rules
- Start with Intent Replay. Before other work in the repository, look for
.project/and the Intent Replay block inAGENTS.md. If either is missing, bootstrap. If both exist, read them and keep the tracking files current as the work changes. Do not skip this step, and do not start a second tracking system. TheAGENTS.mdblock is the standing order that tells the next agent to do the same. A commit or push does not start this update. - Bootstrap safely. At the intended project root, create missing Intent Replay files and the
AGENTS.mdintegration. Never reset, truncate, replace, or delete existing project content. If initialization cannot be performed, explain the blocker and provide the exact proposed changes. - Read before acting. Inspect
AGENTS.md,.project/NOW.md,.project/INTENT.md, the relevant active task, and only the decisions needed for the task. Reconcile the snapshot with the repository and accessible Git state before trusting it. - Validate continuity. After bootstrap and before the final handoff, run the continuity check in references/lifecycle.md. Fix structural problems. A clean check is not proof the implementation works.
- Never fabricate progress. Label facts as observed, reported, planned, or unverified. Never represent an unrun test, remote-only view, or prior agent claim as verified.
- Update at meaningful boundaries, not per message. Update
INTENT.mdwhen the goal, audience, scope, or a constraint actually changes. Record a decision only when a later agent would choose wrong without it. Update tasks when scope, implementation, blockers, or validation changes. RefreshNOW.mdso it matches the current intent and the decisions still in force. Keep both files short. A commit, push, or sync to GitHub is not one of these updates. - Stop after a commit. When the user asks to commit or push, or when a commit or push has just happened, do not edit
.project/or the Intent Replay block afterward. Do not record that the commit happened in intent, decisions, orNOW.md. Leave any uncommitted Intent Replay edits out of that follow-up. The tree stays as the commit left it. - Archive tasks; compact old decisions. Move accepted and verified tasks from
tasks/active/totasks/done/. Keep task IDs and evidence. WhenDECISIONS.mdgrows past about 120 lines, compact superseded decisions, and accepted decisions that no longer affect the work, down to one line each. Keep every decision ID. Do not silently complete work or remove task files. - Respect permissions and conflicts. Do not commit, push, merge, deploy, install integrations, or run destructive commands without authorization. Detect concurrent edits and discrepancies; preserve both sides and request a resolution rather than overwriting.
- Do not store secrets or personal data.
.project/records where the project is and what it is trying to do. Never write secret keys, credentials, tokens, raw environment values, private chat transcripts, or personal data into these files.
What ships with it
7 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.
- 2d ago First seen · 91 lines · 93 tokens per session scan A a0990e651f1c
intent-replay is a skill published in the GitHub repository HashLips/agent-skills (28 stars, last pushed 3d ago), licensed MIT. It adds 93 tokens to every session and 1,779 once invoked, about $0.0004 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 2026-10-23.
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