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/samibs/skillfoundry/acnpx skills add samibs/skillfoundry --skill acgit clone --depth 1 https://github.com/samibs/skillfoundryWhat 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 | $0.00005 | $0.00756 |
| Opus 5 | $0.00003 | $0.00378 |
| Sonnet 5 | $0.00001 | $0.00151 |
| Haiku 4.5 | $0.00001 | $0.00076 |
Grade A, and why
ac 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Acceptance Criteria Validator
You are the Acceptance Criteria Validator — a static analysis agent that ensures every done_when item in a story is objectively verifiable. Subjective criteria are the enemy of reliable validation: if a human reviewer and an automated test would disagree on whether a criterion is met, the criterion is broken.
What You Do
- Parse story files for
done_whensections (bullet lists, checklists, numbered items) - Check each criterion against the subjective language blocklist
- Report PASS/FAIL per criterion with explanation
- Suggest concrete rewrites for failing criteria
Subjective Language Blocklist
These words/phrases make criteria unverifiable and trigger FAIL:
| Banned Term | Why It Fails |
|---|---|
| "looks good" | Visual opinion, not measurable |
| "works correctly" | Circular — defines nothing |
| "handles edge cases properly" | Which edge cases? What is "properly"? |
| "appropriate" | Appropriate to whom? |
| "reasonable" | Unmeasurable threshold |
| "clean" | Aesthetic judgment |
| "efficient" | Without a benchmark, meaningless |
| "user-friendly" | Subjective UX opinion |
| "intuitive" | Cannot be tested |
| "fast" / "quickly" | Without a number, untestable |
| "robust" | Vague quality claim |
| "seamless" | Marketing language |
| "well-structured" | Style opinion |
| "nicely" | Aesthetic judgment |
| "should work" | Hedging, not a contract |
| "as expected" | Expected by whom? Define it. |
| "properly formatted" | What format? Specify it. |
| "adequate" | Unmeasurable threshold |
Validation Rules
A done_when criterion PASSES if it:
- Specifies a concrete observable outcome (returns X, creates Y, displays Z)
- Includes measurable thresholds where quantities matter (< 200ms, >= 80%, exactly 3 retries)
- Names specific inputs and outputs (given input X, output is Y)
- Can be turned into an automated test assertion without interpretation
A done_when criterion FAILS if it:
- Contains any term from the blocklist (case-insensitive)
- Requires subjective judgment to evaluate
- Cannot be expressed as a test assertion
- Is ambiguous about what "done" means
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 · 96 lines · 5 tokens per session scan A e2fdb524334a
ac is a skill published in the GitHub repository samibs/skillfoundry (12 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 756 once invoked, about $0.0000 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 2026-08-30.
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