Restrict the truncation guard to PLAIN scalars. `yaml` attaches a legitimate
trailing comment to quoted and block scalars too, so keying on `.comment` alone
rejected valid frontmatter — `description: "Complete value" # note` was reported
as truncated, telling the author to quote a value that was already quoted. The
regex this replaced tested the same thing by reading the first character; moving
to the node dropped the check rather than encoding it. Verified all three
shapes (double-quoted, single-quoted, block) against the parser, and added the
regression cases.
Also correct the `--max-turns 1` bullet in docs/skill-authoring.md, which
claimed the skill must be the model's first tool CALL. `collectSkillInvocations`
scans every tool_use block in the assistant event without regard to order, so a
turn calling Read and Skill together scores a hit. What actually loses the
sample is needing a tool RESULT first — that decision lands in turn two, which
never runs. The practical advice is unchanged; the mechanism now matches the
collector.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01SyBPs7caoMcyvnp9ArMBhV
Signed-off-by: cliffhall <cliff@futurescale.com>
Corrects the rationale for the five-positive floor, which was wrong. The
claim was that five prompts make a skill's rate flake-tolerant. The
evaluator scores each prompt on its own `passes / RUNS`, so the number of
prompts never enters any rate and cannot affect a threshold crossing —
`RUNS` is the only knob that does. The floor is worth keeping for
breadth: one prompt measures one phrasing, and a description that fires
on a single narrow shape would pass on its one lucky case.
The bad reasoning had reached five places — AGENTS.md, the authoring
checklist, the JSDoc, the check-site comment and the regression test —
plus the emitted message, which asserted it too. All corrected.
Moves `MIN_POSITIVE_CASES` above `validateEvalCases`'s JSDoc. It had been
inserted between the two, so the `@param`/`@returns` block documented the
constant and the function had none.
Replaces the two remaining cases answerable from `AGENTS.md`: `local-dev`'s
web-client-startup case (the rules file gives the exact command) and
`project-structure`'s clients/web-versus-core case (its tree answers it).
Both replacements probed 3/3 and measure 100%.
RUNS=5 skills:eval: 63/63, every local-dev and project-structure case at
100%. local:gate passes, 34/34.
Signed-off-by: cliffhall <cliff@futurescale.com>
Replaces the last `project-structure` case that `AGENTS.md` answers — a
helper used by the CLI and TUI belongs in `core/` per its own directory
tree. This was also the only case below 100% in the previous suite, so
the static reading and the score agreed. The replacement targets the
skill's first stated purpose, locating code, which `AGENTS.md` gives no
method for; it probed 3/3 and now measures 100%.
Extends the truncation guard's tests to `when_to_use`. The guard loops
over both fields but only `description` was pinned, so dropping the
second from that list would have gone unnoticed. The new case pairs a
quoted description with an unquoted `when_to_use` and asserts the
description is not implicated.
Scopes the negative-case requirement in the prose to model-invoked
skills; round 2 scoped the checklist but left this sentence saying
"every skill file".
Refreshes the stale `~2.8k of 4k` listing figure. The other instance is
past-tense about the three-to-nine experiment and stays, now said
explicitly so two numbers do not look like a disagreement.
Records the eval's real blind spot: all three cases review identified as
`AGENTS.md`-answerable were scoring 80-100% when flagged, so a high score
does not clear a case of overlap. The eval can show a case failing; it
cannot show a passing case is well-aimed.
RUNS=5 skills:eval: 63/63. local:gate passes, 34/34.
Signed-off-by: cliffhall <cliff@futurescale.com>
Enforces the five-positive standard this PR established rather than only
describing it. `validateEvalCases` checked `positives === 0`, so a skill
could regress to a single prompt and still pass the gate. At RUNS=5 one
case is 20 points of that skill's reading, so a one- or two-case skill
reports a rate a single flake can move across the threshold — well-formed
to the checker, meaningless as a measurement. Zero positives keeps its
own clearer message so the common mistake keeps its diagnosis.
Scopes the eval requirement to model-invoked skills in the checklist. A
name-only skill has no eval file because nothing can measure a trigger
only a human types, so an eval there is unrunnable rather than redundant.
Aligns checklist item 1 with the `#` hazard from round 1; it named only
the unquoted colon, which is the louder of the two failure modes.
Four test fixtures asserted on one-positive eval files and legitimately
went red; they now build positives from the exported MIN_POSITIVE_CASES.
The all-positive row keeps testing the missing-negative path, with a new
row for the below-floor case. 388/388 script tests pass.
No skill description or eval case changed, so trigger behaviour is
unaffected and skills:eval is unchanged from the previous round.
Signed-off-by: cliffhall <cliff@futurescale.com>
Fixes an unquoted `#` in `board-ops`'s description. YAML reads ` #28`
onward as a comment, so the parser — and Claude Code, which parses the
same way — only ever saw the description up to `Covers board`. Both
board numbers and the option-deletion hazard were absent from the
listing that decides whether the skill fires, and every check stayed
green because they look for an *empty* description and a truncated one
is still non-empty. Quoting restores all 271 characters, which is the
whole of the 3,144 vs 3,234 discrepancy the review flagged.
`parseSkill` now compares the raw scalar against what YAML kept and
errors on any loss, so the next one fails the gate rather than shipping.
Tests cover the truncated case and a legitimately quoted `#`.
Re-aims the `project-structure` Node-backend case, which `AGENTS.md`
answers via its tree and which had oscillated 80 -> 100% across runs —
the partial-overlap signature this PR documents. The replacement names
no internals and probed 4/4.
Makes the documented probe reproduce the harness: the same
`--disallowedTools` flags as `runPrompt` rather than `--allowedTools`,
`printf` instead of zsh's `print -r`, and `jq` over `tool_use` blocks
instead of a `grep` that also matched MCP server names in the init
event. The first-tool-call table is relabelled as an unrestricted probe,
since the eval cannot reach for Bash at all.
RUNS=5 skills:eval: 63/63, 62 at 100%; restoring those 90 characters
displaced nothing.
Signed-off-by: cliffhall <cliff@futurescale.com>
Adds a multi-name filter to `npm run skills:eval` so a set of related
skills can be iterated together, and makes an unknown name a hard error
rather than an empty run — a typo would otherwise report a green 0/0
that reads exactly like a clean pass of the skill that was meant.
Re-aims two `local-dev` cases and one `test-servers` case that partially
overlapped `AGENTS.md`. Partial overlap oscillates across runs rather
than sitting at zero, so it reads as eval noise instead of a defect in
the case; `docs/skill-authoring.md` now records that failure mode and
the observed scores.
Widens the `test-servers` description to the "Use when …" shape and
points the `testing` skill at it, since anything needing a real server
to run against is a procedure `testing` does not carry. That growth
moves the skill listing from 2,921 to 3,144 of 4,000 characters, so the
figure recorded in AGENTS.md is updated to match.
Signed-off-by: cliffhall <cliff@futurescale.com>
`skills:eval` was at 38/46 with several skills measuring 0-33%. The low
scores were mostly bad eval cases rather than bad descriptions, for two
reasons that were nowhere written down:
- The harness runs each case with `--max-turns 1`, so the skill has to be
the model's FIRST tool call. A prompt asking "how does X work here", or
naming a concrete file, sends the model to Read/Grep first — a better
answer to the question, scored as a miss. Every 0% case was one of these.
- `AGENTS.md` is resident on every turn, so a prompt it already answers is
answered without any skill and the case can never pass.
Replace the cases that measured the wrong thing, expand every model-invoked
skill from 3 to 5 positives (negatives kept) so one flake cannot cross the
threshold, and reshape `local-dev`'s description to the action-first form.
Add `docs/skill-authoring.md` with the case shapes that fire, the ones that
cannot, and the probe-then-measure loop.
RUNS=5 npm run skills:eval: 63/63 at or above threshold, 60 at 100%,
all 18 negatives clean.
Closes#2202
Signed-off-by: cliffhall <cliff@futurescale.com>
