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references/complexity_matching.md

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1# Complexity Matching — Compressed vs Full 4-Section Output
2
3This reference answers exactly one decision: **when does capture use the full 4-section format vs the compressed format, and what does each look like in practice?**
4
5Pair with `scripts/complexity_estimator.py` for the deterministic recommendation.
6
7## The Core Rule
8
9> **Match output complexity to input complexity.**
10
11A 30-item dump with natural clusters needs the full 4-section structure to be useful. A 5-item dump of unrelated todos drowns in that structure — the format becomes ceremony, not signal. Force-fitting structure on a small dump makes the skill feel bureaucratic.
12
13## When to Use Each Format
14
15| Signal | Recommended format |
16|---|---|
17| 8+ items AND natural clustering exists (3+ items share a theme) | Full 4-section |
18| 8+ items but NO clustering (all unrelated todos) | Compressed (with explicit "no clusters" note) |
19| 5–7 items, mixed kinds, some clustering | Either — judgment call. Lean compressed unless clusters are strong. |
20| ≤5 items, unrelated | Compressed |
21| ≤5 items but all related to one project | Compressed with single project header |
22| Workspace inaccessible AND ≤5 items | Compressed with no Section 3 (still note "no workspace accessible") |
23
24`complexity_estimator.py` returns `format=full` or `format=compressed` based on item count + clustering signal. Use it as the seed; override with judgment when context warrants.
25
26## Format A: Full 4-Section
27
28Use for substantive dumps with real structure. Roughly:
29
30```
31## Projects & Ideas
32
33### {Project A in user's voice}
34- {component}
35- {component}
36- Q: {open question}
37- Decide: {decision}
38
39### {Project B}
40- ...
41
42## Tasks
43
44- {task} [Project: A]
45- {task}
46- Decide: {decision}
47- Resolve: {open question}
48
49## Connections
50
51- {file/folder}: {real workspace match}
52- (or) "No connections found — workspace inventory clean."
53- (or) "No workspace accessible from here..."
54
55## How I Can Help
56
57- {concrete offer: what + where}
58- {concrete offer: what + where}
59
60**Which of these should I tackle?**
61```
62
63## Format B: Compressed
64
65Use for small or unrelated dumps. Roughly:
66
67```
68## What I heard
69
70- {item}
71- {item}
72- {item}
73- Decide: {decision}
74- Resolve: {open question}
75
76## How I can help
77
78- {concrete offer: what + where}
79- {concrete offer: what + where}
80
81Which should I tackle?
82```
83
84Notice what's gone in compressed:
85- No project headers (nothing to cluster)
86- No separate Tasks section (the list IS the tasks)
87- No Connections section as a separate header (if there's a real connection, fold it inline as a parenthetical: `- fix the auth bug (relates to src/auth/login.ts:42)`)
88- Same Section 4 discipline (concrete offers + directive question)
89
90## Worked Examples
91
92### Example 1: Full 4-section (12 items, 2 clusters)
93
94**Dump:**
95> "Ok so dump time. Q3 launch is approaching — need to nail down pricing, draft the launch email, brief Sarah on the marketing angle. Also ferret app keeps nagging me — should I talk to my cofounder about it, or is it just procrastination? Need to sketch the matching algorithm if it's serious. Also: fix the damn auth bug, rewrite the login form because it's ugly, write tests for both. Oh and I should probably do my Q3 OKRs before launch."
96
97**Extracted items:** 11
98**Clusters:** Q3 Launch (4 items), Ferret App (3 items), Auth (3 items), OKRs (1 item)
99**Recommendation:** `format=full` (clear clustering, 11 items)
100
101**Output:**
102
103```
104## Projects & Ideas
105
106### Q3 Launch
107- Nail down pricing
108- Draft launch email
109- Brief Sarah on the marketing angle
110- Decide: Q3 OKRs (do before launch)
111
112### Ferret App
113- Talk to cofounder OR decide it's procrastination
114- Sketch matching algorithm (if serious)
115- Decide: serious project or shelf?
116
117### Auth Fixes
118- Fix the auth bug
119- Rewrite the login form (it's ugly)
120- Write tests for the bug fix + login rewrite
121
122## Tasks
123
124- Nail down Q3 pricing [Project: Q3 Launch]
125- Draft Q3 launch email [Project: Q3 Launch]
126- Brief Sarah on Q3 marketing angle [Project: Q3 Launch]
127- Talk to cofounder about ferret app [Project: Ferret App]
128- Sketch ferret matching algo [Project: Ferret App]
129- Fix auth bug [Project: Auth Fixes]
130- Rewrite login form [Project: Auth Fixes]
131- Write tests for auth + login [Project: Auth Fixes]
132- Do Q3 OKRs (before launch)
133- Decide: ferret app — serious or shelf?
134
135## Connections
136
137(workspace inventory results go here — only verified matches)
138
139## How I Can Help
140
141- I can draft the Q3 launch email as a 1-pager. Output: chat reply.
142- I can pull together a pricing-options matrix from your existing pricing docs. Output: `docs/q3-pricing-options.md`.
143- I can scaffold the ferret matching algorithm sketch (input → match score). Output: `docs/ferret-algo-sketch.md`.
144
145**Which of these should I tackle?**
146```
147
148### Example 2: Compressed (5 unrelated items)
149
150**Dump:**
151> "Quick: email Sarah, fix that test that's been flaky, decide between Postgres and Mongo for the new service, dentist appointment, finish reading the article on RAG."
152
153**Extracted items:** 5
154**Clusters:** none (all unrelated)
155**Recommendation:** `format=compressed`
156
157**Output:**
158
159```
160## What I heard
161
162- Email Sarah
163- Fix the flaky test
164- Decide: Postgres or Mongo for the new service
165- Dentist appointment
166- Finish reading the RAG article
167
168## How I can help
169
170- I can draft the email to Sarah if you tell me what it's about. Output: chat reply.
171- I can investigate the flaky test (likely racing condition; happy to reproduce). Output: chat analysis + fix PR if you want.
172- I can pull a Postgres-vs-Mongo decision matrix scoped to your service requirements. Output: chat reply or `docs/db-decision.md`.
173
174Which should I tackle?
175```
176
177Notice the dentist appointment + RAG article aren't in "How I can help" — there's nothing meaningful to offer on those. That's correct: don't pad Section 4 with offers for things that don't need help.
178
179### Example 3: Compressed but workspace-aware (small + connection inline)
180
181**Dump:**
182> "auth bug is back, need to look at the login flow again, also I want to add 2FA finally."
183
184**Extracted items:** 3
185**Clusters:** all auth-related but only 3 items
186**Recommendation:** `format=compressed`
187
188**Output:**
189
190```
191## What I heard (Auth)
192
193- Auth bug is back (relates to `src/auth/login.ts` based on workspace inventory)
194- Look at login flow again
195- Add 2FA
196
197## How I can help
198
199- I can reproduce the auth bug from the symptoms — what's the failure mode? Output: chat repro + fix.
200- I can sketch a 2FA implementation matching your existing auth pattern (TOTP via the same provider you use). Output: `docs/2fa-sketch.md`.
201
202Which should I tackle?
203```
204
205Notice the workspace connection got folded inline as a parenthetical instead of a separate Section 3 header. That's the compressed-with-context pattern.
206
207## Operational Checklist
208
209Before delivering output:
210
211- [ ] Run `complexity_estimator.py` (or apply the Signal table above)
212- [ ] If `format=compressed`, do NOT force the 4-section format
213- [ ] If `format=full`, ensure the clusters are real (3+ items per cluster) — don't invent clusters to fill the format
214- [ ] Either way, Section 4 ("How I can help") MUST have concrete offers with what + where
215- [ ] Either way, end with the directive question
216
217## Why This Matters
218
219A skill that returns the same format regardless of input is a template, not a skill. The reason capture is useful is that it adapts to the dump's actual shape. When a 5-item list comes back wrapped in 4 elaborate empty-feeling sections, the user learns to distrust the skill. When a 30-item dump comes back as a flat compressed list, the user learns the skill can't actually handle complexity.
220
221Match the output to the input, every time.

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