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 chan4lk/specclaw --skill rulesgit clone --depth 1 https://github.com/chan4lk/specclawWrote 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/chan4lk/specclaw/rules)<a href="https://agentmods.dev/skills/chan4lk/specclaw/rules"><img src="https://agentmods.dev/badge/skills/chan4lk/specclaw/rules/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/chan4lk/specclaw/rules"><img src="https://agentmods.dev/badge/skills/chan4lk/specclaw/rules.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 | $0.00036 | $0.00968 |
| Opus 5.5 | $0.00014 | $0.00387 |
| Sonnet 5.5 | $0.00007 | $0.00194 |
| Haiku 4.5 | $0.00004 | $0.00097 |
Grade A, and why
rules 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 today.
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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
specclaw-review: create the review rules for this repo
Two outputs:
- a
review:block in.specclaw/config.yaml; - rule files in
.specclaw/review/rules/.
Write nothing until the user approves what you show them. Rules decide what blocks a PR, so a rule written silently is a gate nobody agreed to.
1. Find what the repo already has
Run specclaw-review discover (or call the review_discover MCP tool). It is read-only and lists:
- the repo's governance by category, with sizes;
- whether those files define severity;
- the existing repo rules;
- how much review history is on disk;
- whether a PR host can be read;
- a suggested
review:block.
Before you go on:
- Existing governance wins. Point
review.governanceat those files; they are read fresh every round. Never copy them into rule files, because copies go stale. - Decisions/ADRs stay out of the rulebook. List them for the user. The reviewer reads them with its tools when a change touches them.
- Over budget: if the inlined size is over budget, propose which files to drop from
governance, smallest value first: long guides, tutorials, and anything an ADR already covers. Show the size before and after. - Severity: if the files define severity, say so.
severity: projectapplies by default, so the repo's own idea of "major" wins.
2. Draft rules only for what is missing
Mine the repo's own review history and write down what it keeps getting wrong. Never write generic advice; the plugin's packs already hold that.
Sources, best first:
- Past PR review comments. Read only, last 30–50 merged PRs.
- GitHub:
gh api "repos/{owner}/{repo}/pulls/comments?per_page=100" --paginate -q '.[] | [.path, .body] | @tsv' | head -2000 - Azure DevOps: use the token in
.specclaw/.env(AZDO_TOKEN; never print it). Read_apis/git/repositories/<repo>/pullrequests?searchCriteria.status=completed&$top=50, then each PR's/threads.
- GitHub:
- The repo's records:
.specclaw/changes/*/learnings.mdandfollow-ups.md. - Recurring lessons in review memory:
specclaw-review learn(dry-run).
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.
- today First seen · 81 lines · 36 tokens per session scan A ced0b77329e3
rules is a skill published in the GitHub repository chan4lk/specclaw (13 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 968 once invoked, about $0.0001 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 2026-10-17.
Other skills, from other repositories
review-spec
A workflow for comparing an existing feature specification with the project's review checklist and implementation. It looks for missing requirements, differences between documented screens and actual screens, and lessons from rejected submissions.
loki-mode
An autonomous software factory that knows what it is supposed to deliver, and proves it did. Use when the user says Loki Mode or asks to build, fix or verify software autonomously.
next-steps
Have the Product Owner survey the project's current state — shipped features, in-flight work, open findings, standing goals — and propose one concrete next feature ready to hand to /story-time or /spark. Use when the user has no idea in hand and asks "what should we build next", when a loop just closed and the next…
increment
Start the Act phase of the SPARK loop: build the increment strictly following the approved plan, task by task, with progress tracked in the plan. Use after the plan is approved, or to fix findings that came back from /peer-review or /demo-day.
checkpoint-mode
Pause for review every N tasks - selective autonomy pattern.
loki-mode
Launch Loki Mode autonomous SDLC agent. Handles PRD-to-deployment with minimal human intervention. Invoke for multi-phase development tasks, bug fixing campaigns, or full product builds.